Testing, Deployment, and Maintenance

The DORA metrics are pretty much an iceberg, with the five indicators sticking out above the surface and plenty of research hidden beneath the waves. With the amount of work that has been put into that program, the whole thing can seem fairly opaque when you start working with them. Let’s try to peek under the surface and see what’s going on down there. After our last post about metrics, we thought it might be interesting to look at how metrics are used on different organizational levels. If we start from the top, DORA is one of the more popular projects today. Here, we’ll tell you some ideas we’ve had on how to use the DORA metrics, but first, there have been some questions we’ve been asking ourselves about the research and its methodology. We’d like to share those questions with you, starting with: What Is DORA? DevOps Research and Assessment is a company founded in 2015. Since then, they have been publishing State of DevOps reports, in which they’ve analyzed development trends in the software industry. In 2018, the people behind that research published a book (Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations) where they identified key metrics that have the strongest influence on business performance: Deployment Frequency (DF): How often your team deploys to production. Mean Lead Time for Changes (MLT): How long it takes for a commit to get to production. Together with DF, those are measures of velocity. Change Failure Rate (CFR): The number of times your users were negatively affected by changes, divided by the number of changes. Mean Time to Restore (MTTR): How quickly service was restored after each failure. This and CFR are measures of stability. Reliability: The degree to which a team can keep promises and assertions about the software they operate. This is the most recent addition to the list. The DORA team has conducted a truly impressive amount of work. They’ve done solid academic research, and, in the reports, they always honestly present all results, even when those seem to counter their hypotheses. All that work and the volume of data processed truly is impressive, but, ironically, it might present a limitation. When the DORA team applies their metrics, they back it up with detailed knowledge; this is absent when someone else uses them. It is not a hypothetical situation because, by now, these metrics are so popular that tools have been written specifically to measure them—like fourkeys. More general tools, like GitLab or Codefresh, can also track them out of the box. The questions we’re about to ask might be construed as criticism—but that is not the intention. We’re just trying to show that DORA is a complex tool, which should be, as they say, handled with care. Do the Key Metrics Work Everywhere? The main selling point of the key metrics is their universal importance. DORA found them to be significant for all types of enterprises, regardless of whether we’re talking about a Silicon Valley startup, a multinational corporation, or a government. This would imply that those metrics could work as an industry standard. In a way, this is how they are presented: the companies in DORA surveys are grouped into clusters (usually four), from low to elite, and the values for the elite cluster look like something everyone should emulate. But, in reality, all of this is more descriptive than imperative. The promoted value, for e.g., Mean Lead Time for Changes, is simply the value from companies that were grouped into the elite cluster, and that value can change from year to year. For instance, in 2019, it was less than one day. In 2018 and 2021, less than one hour, and in 2022—between one day and one week. By the way, that last one is because there was no “elite” cluster at all that year, the data looked more convenient in three clusters. So, if we stop at this point and don’t look further than the key metrics, we just get the message—here’s a picture of what an elite DevOps team looks like, let’s be more like them, everybody. In the end, we’re coming back to the simple truth that correlation does not imply causation. If the industry leaders that have embraced DevOps all display these stats, does it mean that by gaining those stats you will also become a leader? Doing it without proper understanding or regard for context might result in wasted effort. How much will it cost you to drive each of those metrics to the elite level—and keep them there indefinitely? What will the return on that investment be? To answer those questions, you’re going to need to dig deeper—and the same goes for the next question on our list. We Know How Fast We Are Going—But in What Direction? Not only will you need something lower-level to complement the DORA metrics, you’ll also need something higher-level. As we’ve already said previously, a good metric should somehow be tied to user happiness. The problem is, that the DORA metrics tell you nothing about business context—whether or not you’re responding to any kind of real demand out there. Using just DORA to set OKRs will paint a very incomplete picture of how well the business is performing. You’ll know how fast you’re going, but you might be driving in the opposite direction from where you need to be, and the DORA metrics won’t alert you to that. What Is Reliability? This is what we’ve been asking ourselves when we were researching the fifth DORA metric. If you’ve read the 2021 and 2022 reports, you’ll know that it is something that was inspired by Google’s Site Reliability Engineering (SRE), but you’ll still be none the wiser as to what specific metrics it is based on, how exactly it is calculated, or how you might go about measuring your own reliability. The reports don’t show any values for it, it is not shown in the nice tables where they compare clusters, and the Quick Check offered by DORA doesn’t mention reliability at all in its main questions. The last State of DevOps report states that investment in SRE yields improvements to reliability “only once a threshold of adoption has been reached,” but doesn’t tell us what that threshold is. This leaves a company in the dark as to whether they’ll gain anything by investing in SRE. This is not to be taken as criticism of SRE—the way it’s presented in the reports is opaque, and if you want to make any meaningful decisions in your team, you’ll need to drill down to more actionable metrics. Idea: Check the DORA Metrics Against Each Other The great thing about the key DORA metrics is how they can serve as checks for each other; each of them taken on its own would lie by omission. A team of very careful developers who are diligent with their unit tests and who are supported by a qualified QA team could have the same deployment frequency and lead time for changes as a team who does no testing whatsoever. Obviously, the products delivered by those teams would be very different. So, we need a measure of how much user experience is affected by change. Time to Restore tells us something about it—but on its own, it is useless, like measuring distance instead of speed. Spending two hours restoring a failure that happened once a month or once a day are two completely different things. Change Failure Rate to the rescue—it tells us how often the changes happen. Another problem with MTTR: you could have a low value that is achieved by fixing every disaster with an emergency hack. Or you could have a high deployment frequency, which allows you to roll out stable fixes in a quick and reliable manner. This is an extremely important advantage of a high DF: being able to respond to situations in the field in a non-emergency manner. Again, the metrics serve as checks for each other. Further, if we’re trying to gauge the damage from failures, we need to know how much every minute of downtime costs us. Figuring it out will require additional research, but it will put the figures into proper context and will finally allow us to make a decision based on the numbers. So, for the DORA metrics to be used, they need to be judged against each other and against additional technical, product, and marketing metrics. Idea: Know the Limits of Applicability Taking this point further, there are situations where the DORA metrics don’t necessarily indicate success or failure. The most obvious example here is that your deployment frequency depends not only on your internal performance but also on your client’s situation. If they are only accepting changes once per quarter, there isn’t much you can do about that. The last State of DevOps report recommends using cloud computing for improving organizational performance (which includes deployment frequency). This makes sense, of course—but clouds are not always an option, and this should be considered when judging your DF. If we take Qameta Software as an example, Allure TestOps has a cloud-based version, where updating is a relatively easy affair. However, if you want to update an on-premises version, you’ll need to work with the admins and it will take a while. Moreover, some clients simply decide they want to stay with an older version of TestOps for fear of backward compatibility problems. Wrike has about 70k tests, which are launched several times a day with TestOps. Any disruption to that process will have an extremely high cost, so they’ve made the decision not to update. There are other applications for which update frequency also isn’t as high a priority—like offline computer games. All in all, there are situations where chasing the elite status measured by DORA might do more harm than good. This doesn’t mean that the metrics themselves become useless, just that they have to be used with care. Idea: Use the DORA Metrics as an Alarm Bell The DORA metrics are lagging indicators, not leading ones. This means they don’t react to change immediately, and only show the general direction of where things are going. To be useful, they have to be backed by other, more sensitive and local indicators. If we take a look at some real examples, we’ll see that the key metrics are often used as an alarm bell. At first, people making decisions get dissatisfied with the values they’re seeing. Maybe they want to get into a higher DORA cluster, or maybe one particular metric used to show higher values in the past. So they start digging into the problem and drilling down: either talk to people in their teams or look up lower-level metrics, such as throughput, flow efficiency, work in progress, number of merges to trunk per developer per day, bugs found pre-prod vs in prod, etc. This helps identify real issues and bottlenecks. If you want to know what actions you can take to address your issues, DORA is offering a list of DevOps capabilities, which range from techniques like version control and CI to cultural aspects like improving job satisfaction. Conclusion Of course, there is a reason why some stuff, like reliability, might be opaque and why some technical details might be black-boxed (or gray-boxed) in the DORA metrics. It seems like they were created for a fairly high organizational level. At some point, the authors state explicitly that the DORA assessment tool was designed to target business leaders and executives. This, combined with its academic background, makes it an impressive and complex tool. To use that tool properly, we, as always, have to be aware of its limitations. We have to know that, in some situations, a non-elite value for the key metrics might be perfectly acceptable, and chasing elite status might cost more than that chase will yield. Business context and customer needs have to be held above these metrics. The metrics have to be checked against each other and against more local and leading indicators. If all of this is kept in mind, the DORA metrics can provide a useful measure of your operational performance. We plan to continue this series of articles about metrics in QA. The previous one outlined a series of requirements for a good metric; and now, we want to cover specific examples of metrics on different organizational levels. After DORA, we’ll probably want to go for something lower-level, so stay tuned!

As with back-end development, observability is becoming increasingly crucial in front-end development, especially when it comes to troubleshooting. For example, imagine a simple e-commerce application that includes a mobile app, web server, and database. If a user reports that the app is freezing while attempting to make a purchase, it can be challenging to determine the root cause of the problem. That's where OpenTelemetry comes in. This article will dive into how front-end developers can leverage OpenTelemetry to improve observability and efficiently troubleshoot issues like this one. Why Front-End Troubleshooting? Similar to back-end development, troubleshooting is a crucial aspect of front-end development. For instance, consider a straightforward e-commerce application structure that includes a mobile app, a web server, and a database. Suppose a user reported that the app is freezing while attempting to purchase a dark-themed mechanical keyboard. Without front-end tracing, we wouldn't have enough information about the problem since it could be caused by different factors such as the front-end or back-end, latency issues, etc. We can try collecting logs to get some insight, but it's challenging to correlate client-side and server-side logs. We might attempt to reproduce the issue from the mobile application, but it could be time-consuming and impossible if the client-side conditions aren't available. However, if the issue isn't reproduced, we need more information to identify the specific problem. This is where front-end tracing comes in handy because, with the aid of front-end tracing, we can stop making assumptions and instead gain clarity on the location of the issue. Front-End Troubleshooting With Distributed Tracing Tracing data is organized in spans, which represent individual operations like an HTTP request or a database query. By displaying spans in a tree-like structure, developers can gain a comprehensive and real-time view of their system, including the specific issue they are examining. This allows them to investigate further and identify the cause of the problem, such as bottlenecks or latency issues. Tracing can be a valuable tool for pinpointing the root cause of an issue. The example below displays three simple components: a front-end a back-end, and a database. When there is an issue, the trace encompasses spans from both the front-end app and the back-end service. By reviewing the trace, it's possible to identify the data that was transmitted between the components, allowing developers to follow the path from the specific user click in the front-end to the DB query. Rather than relying on guesswork to identify the issue, with tracing, you can have a visual representation of it. For example, you can determine whether the request was sent out from the device, whether the back-end responded, whether certain components were missing from the response and other factors that may have caused the app to become unresponsive. Suppose we need to determine if a delay caused a problem. In Helios, there's a functionality that displays the span's duration. Here's what it looks like: Now you can simply analyze the trace to pinpoint the bottleneck. In addition, each span in the trace is timestamped, allowing you to see exactly when each action took place and whether there were any delays in processing the request. Helios comes with a span explorer that was created explicitly for this purpose. The explorer enables the sorting of spans based on their duration or timestamp: The trace visualization provides information on the time taken by each operation, which can help identify areas that require optimization. A default view available in Jaeger is also an effective method to explore all the bottlenecks by displaying a trace breakdown. Adding Front-End Instrumentation to Your Traces in OpenTelemetery: Advanced Use Cases It's advised to include front-end instrumentation in your traces to enhance the ability to analyze bottlenecks. While many SDKs provided by OpenTelemetry are designed for back-end services, it's worth noting that OpenTelemetry has also developed an SDK for JavaScript. Additionally, they plan to release more client libraries in the future. Below, we will look at how to integrate these libraries. Aggregating Traces Aggregating multiple traces from different requests into one large trace can be useful for analyzing a flow as a whole. For instance, imagine a purchasing process that involves three REST requests, such as validating the user, billing the user, and updating the database. To see this flow as a single trace for all three requests, developers can create a custom span that encapsulates all three into one flow. This can be achieved using a code example like the one below. const { createCustomSpan } = require('@heliosphere/web-sdk'); const purchaseFunction = () => { validateUser(user.id); chargeUser(user.cardToken); updateDB(user.id); }; createCustomSpan("purchase", {'id': purchase.id}, purchaseFunction); From now on, the trace will include all the spans generated under the validateUser, chargeUser, and updateDB categories. This will allow us to see the entire flow as a single trace rather than separate ones for each request. Adding Span Events Adding information about particular events can be beneficial when investigating and analyzing front-end bottlenecks. With OpenTelemetry, developers can utilize a feature called Span Event, which allows them to include a report about an event and associate it with a specific span. A Span Event is a message on a span that describes a specific event with no duration and can be identified by a single time stamp. It can be seen as a basic log and appears in this format: const activeSpan = opentelemetry.trace.getActiveSpan(); activeSpan.addEvent('User clicked Purchase button); Span Events can gather various data, such as clicks, device events, networking events, and so on. Adding Baggage Baggage is a useful feature provided by OpenTelemetry that allows adding contextual information to traces. This information can be propagated across all spans in a trace and can be helpful in transferring user data, such as user identification, preferences, and Stripe tokens, among other things. This feature can benefit front-end development since user data is a crucial element in this area. You can find more information about Baggage right here. Deploying Front-End Instrumentation Deploying the instrumentation added to your traces is straightforward, just like deploying any other OpenTelemetry SDK. Additionally, you can use Helios's SDK to visualize and gain more insights without setting up your own infrastructure. To do this, simply visit the Helios website, register, and follow the steps to install the SDK and add the code snippet to your application. The deployment instructions for the Helios front-end SDK are shown below: Where to Go From Here: Next Steps for Front-End Developers Enabling front-end instrumentation is a simple process that unlocks a plethora of new troubleshooting capabilities for full-stack and front-end developers. It allows you to map out a transaction, starting from a UI click and to lead up to a specific database query or scheduled job, providing unique insights for bottleneck identification and issue analysis. Both OpenTelemetry and Helios support front-end instrumentation, making it even more accessible for developers. Begin utilizing these tools today to enhance your development workflow.

Three Hard Facts First, the complexity of your software systems is through the roof, and you have more external dependencies than ever before. 51% of IT professionals surveyed by SolarWinds in 2021 selected IT complexity as the top issue facing their organization. Second, you must deliver faster than the competition, which is increasingly difficult as more open-source and reusable tools let small teams move extremely fast. Of the 950 IT professionals surveyed by RedHat, only 1% indicated that open-source software was “not at all important.” And third, reliability is slowing you down. The Reliability/Speed Tradeoff In the olden days of software, we could just test the software before a release to ensure it was good. We ran unit tests, made sure the QA team took a look, and then we’d carefully push a software update during a planned maintenance window, test it again, and hopefully get back to enjoying our weekend. By 2023 standards, this is a lazy pace! We expect teams to constantly push new updates (even on Fridays) with minimal dedicated manual testing. They must keep up with security patches, release the latest features, and ensure that bug fixes flow to production. The challenge is that pushing software faster increases the risk of something going wrong. If you took the old software delivery approach and sped it up, you’d undoubtedly have always broken releases. To solve this, modern tooling and cloud-native infrastructure make delivering software more reliable and safer, all while reducing the manual toil of releases. According to the 2021 State of DevOps report, more than 74% of organizations surveyed have Change Failure Rate (CFR) greater than 16%. For organizations seeking to speed up software changes (see DORA metrics), many of these updates caused issues requiring additional remediation like a hotfix or rollback. If your team hasn’t invested in improving the reliability of software delivery tooling, you won’t be able to achieve reliable releases at speed. In today’s world, all your infrastructure, including dev/test infrastructure, is part of the production environment. To go fast, you also have to go safely. More minor incremental changes, automated release and rollback procedures, high-quality metrics, and clearly defined reliability goals make fast and reliable software releases possible. Defining Reliability With clearly defined goals, you will know if your system is reliable enough to meet expectations. What does it mean to be up or down? You have hundreds of thousands of services deployed in clouds worldwide in constant flux. The developers no longer coordinate releases and push software. Dependencies break for unexpected reasons. Security fixes force teams to rush updates to production to avoid costly data breaches and cybersecurity threats. You need a structured, interpreted language to encode your expectations and limits of your systems and automated corrective actions. Today, definitions are in code. Anything less is undefined. The alternative is manual intervention, which will slow you down. You can’t work on delivering new features if you’re constantly trying to figure out what’s broken and fix releases that have already gone out the door. The most precious resource in your organization is attention, and the only way to create more is to reduce distractions. Speeding Up Reliably Service level objectives (SLOs) are reliability targets that are precisely defined. SLOs include a pointer to a data source, usually a query against a monitoring or observability system. They also have a defined threshold and targets that clearly define pass or fail at any given time. SLOs include a time window (either rolling or calendar aligned) to count errors against a budget. OpenSLO is the modern de facto standard for declaring your reliability targets. Once you have SLOs to describe your reliability targets across services, something changes. While SLOs don’t improve reliability directly, they shine a light on the disconnect between expectations and reality. There is a lot of power in simply clarifying and publishing your goals. What was once a rough shared understanding becomes explicitly defined. We can debate the SLO and decide to raise, lower, redefine, split, combine, and modify it with a paper trail in the commit history. We can learn from failures as well as successes. Whatever other investments you’re making, SLOs help you measure and improve your service. Reliability is engineered; you can’t engineer a system without understanding its requirements and limitations. SLOs-as-code defines consistent reliability across teams, companies, implementations, clouds, languages, etc.

The Southwest Airlines fiasco from December 2022 and the FAA Notam database fiasco from January 2023 had one thing in common: their respective root causes were mired in technical debt. At its most basic, technical debt represents some kind of technology mess that someone has to clean up. In many cases, technical debt results from poorly written code, but more often than not, it is more a result of evolving requirements that older software simply cannot keep up with. Both the Southwest and FAA debacles centered on legacy systems that may have met their respective business needs at the time they were implemented but, over the years, became increasingly fragile in the face of changing requirements. Such fragility is a surefire result of technical debt. The coincidental occurrence of these two high-profile failures mere weeks apart lit a fire under organizations across both the public and private sectors to finally do something about their technical debt. It’s time to modernize, the pundits proclaimed, regardless of the cost. Ironically, at the same time, a different set of pundits, responding to the economic slowdown and prospects of a looming recession, recommended that enterprises delay modernization efforts in order to reduce costs short term. After all, modernization can be expensive and rarely delivers the type of flashy, top-line benefits the public markets favor. How, then, should executives make decisions about cleaning up the technical debt in their organizations? Just how important is such modernization in the context of all the other priorities facing the C-suite? Understanding and Quantifying Technical Debt Risk Some technical debt is worse than others. Just as getting a low-interest mortgage is a much better idea than loan shark money, so too with technical debt. After all, sometimes shortcuts when writing code are a good thing. Quantifying technical debt, however, isn’t a matter of somehow measuring how messy legacy code might be. The real question is one of the risk to the organization. Two separate examples of technical debt might be just as messy and equally worthy of refactoring. But the first example may be working just fine, with a low chance of causing problems in the future. The other one, in contrast, could be a bomb waiting to go off. Measuring the risks inherent in technical debt, therefore, is far more important than any measure of the debt itself — and places this discussion into the broader area of risk measurement or, more broadly, risk scoring. Risk scoring begins with risk profiling, which determines the importance of a system to the mission of the organization. Risk scoring provides a basis for quantitative risk-based analysis that gives stakeholders a relative understanding of the risks from one system to another — or from one area of technical debt to another. The overall risk score is the sum of all of the risk profiles across the system in question — and thus gives stakeholders a way of comparing risks in an objective, quantifiable manner. One particularly useful (and free to use) resource for calculating risk profiles and scores is Cyber Risk Scoring (CRS) from NIST, an agency of the US Department of Commerce. CRS focuses on cybersecurity risk, but the folks at NIST have intentionally structured it to apply to other forms of risk, including technical debt risk. Comparing Risks Across the Enterprise As long as an organization has a quantitative approach to risk profiling and scoring, then it’s possible to compare one type of risk to another — and, furthermore, make decisions about mitigating risks across the board. Among the types of risks that are particularly well-suited to this type of analysis are operational risk (i.e., risk of downtime), which includes network risk; cybersecurity risk (the risk of breaches); compliance risk (the risk of out-of-compliance situations); and technical debt risk (the risk that legacy assets will adversely impact the organization). The primary reason to bring these various sorts of risks onto a level playing field is to give the organization an objective approach to making decisions about how much time and money to spend on mitigating those risks. Instead of having different departments decide how to use their respective budgets to mitigate the risks within their scope of responsibility, organizations require a way to coordinate various risk mitigation efforts that leads to an optimal balance between risk mitigation and the costs for achieving it. Calculating the Threat Budget Once an organization looks at its risks holistically, one uncomfortable fact emerges: it’s impossible to mitigate all risks. There simply isn’t enough money or time to address every possible threat to the organization. Risk mitigation, therefore, isn’t about eliminating risk. It’s about optimizing the amount of risk we can’t mitigate. Optimizing the balance between mitigation and the cost of achieving it across multiple types of risk requires a new approach to managing risk. We can find this approach in the practice of Site Reliability Engineering (SRE). SRE focuses on managing reliability risk, a type of operational risk concerned with reducing system downtime. Given the goal of zero downtime is too expensive and time-consuming to achieve in practice, SRE calls for an error budget. The error budget is a measure of how far short of perfect reliability the organization targets, given the cost considerations of mitigating the threat of downtime. If we generalize the idea of error budgets to other types of risk, we can postulate a threat budget which represents a quantitative measure of how far short of eliminating a particular risk the organization is willing to tolerate. Intellyx calls the quantitative, best practice approach to managing threat budgets across different types of risks threat engineering. Assuming an organization has leveraged the risk scoring approach from NIST (or some alternative approach), it’s now possible to engineer risk mitigation across all types of threats to optimize the organization’s response to such threats. Applying Threat Engineering to Technical Debt Resolving technical debt requires some kind of modernization effort. Sometimes this modernization is a simple matter of refactoring some code. In other cases, it’s a complex, difficult migration process. There are several other approaches to modernization with varying risk/reward profiles as well. Risk scoring provides a quantitative assessment of just how important a particular modernization effort is to the organization, given the threats inherent in the technical debt in question. Threat engineering, in turn, gives an organization a way of placing the costs of mitigating technical debt risks in the context of all the other risks facing the organization — regardless of which department or budget is responsible for mitigating one risk or another. Applying threat engineering to technical debt risk is especially important because other types of risk, namely cybersecurity and compliance risk, get more attention and, thus, a greater emotional reaction. It’s difficult to be scared of spaghetti code when ransomware is in the headlines. As the Southwest and FAA debacles show, however, technical debt risk is every bit as risky as other, sexier forms of risk. With threat engineering, organizations finally have a way of approaching risk holistically in a dispassionate, best practice-based manner. The Intellyx Take Threat engineering provides a proactive, best practice-based approach to breaking down the organizational silos that naturally form around different types of risks. Breaking down such silos has been a priority for several years now, leading to practices like NetSecOps and DevSecOps that seek to leverage common data and better tooling to break down the divisions between departments. Such efforts have always been a struggle because these different teams have long had different priorities — and everyone ends up fighting for a slice of the budget pie. Threat engineering can align these priorities. Once everybody realizes that their primary mission is to manage and mitigate risk, then real organizational change can occur. Copyright © Intellyx LLC. Intellyx is an industry analysis and advisory firm focused on enterprise digital transformation. Covering every angle of enterprise IT from mainframes to artificial intelligence, our broad focus across technologies allows business executives and IT professionals to connect the dots among disruptive trends. As of the time of writing, none of the organizations mentioned in this article is an Intellyx customer. No AI was used to produce this article.

Are you looking to get away from proprietary instrumentation? Are you interested in open-source observability, but lack the knowledge to just dive right in? This workshop is for you, designed to expand your knowledge and understanding of open-source observability tooling that is available to you today. Dive right into a free, online, self-paced, hands-on workshop introducing you to Prometheus. Prometheus is an open-source systems monitoring and alerting tool kit that enables you to hit the ground running with discovering, collecting, and querying your observability today. Over the course of this workshop, you will learn what Prometheus is, what it is not, install it, start collecting metrics, and learn all the things you need to know to become effective at running Prometheus in your observability stack. Previously, I shared an introduction to Prometheus in a lab that kicked off this workshop. In this article, you'll be installing Prometheus from either a pre-built binary from the project or using a container image. I'm going to get you started on your learning path with this first lab that provides a quick introduction to all things needed for metrics monitoring with Prometheus. Note this article is only a short summary, so please see the complete lab found online here to work through it in its entirety yourself: The following is a short overview of what is in this specific lab of the workshop. Each lab starts with a goal. In this case, it is fairly simple: This lab guides you through installing Prometheus on your local machine, configuring, and running it to start gathering metrics. You are confronted right from the start with two possible paths to installing the Prometheus tooling locally on your machine: using a pre-compiled binary for your machine's architecture, or using a container image. Installing Binaries The first path you can take to install Prometheus on your local machine is to obtain the right version of the pre-compiled binaries for your machine architecture. I've provided the links to directly obtain Mac OSX, Linux, and Windows binaries. The installation is straightforward. You'll learn what a basic configuration looks like while creating your own to get started with scraping your first metrics from the Prometheus server itself. Once it's up and running, you'll explore the basic information available to you through the Prometheus status pages, a web console. You explore how to verify that your configured scraping target is up and running, then go and break your configuration to see what a broken target looks like on the web console status page. Next, you browse the available configuration flags for running your Prometheus server, look at the time series database status, explore your active configuration, and finish up by playing with some yet-to-be-explained query expressions in the provided tooling. That last exercise is more extensive than just pasting in queries, you'll learn about built-in validation mechanisms and explore the graphing visualization offered out of the box. This lab completes with you having an installed binary package for your machine's architecture, a running Prometheus with a basic configuration, and an understanding of the available tooling in the provided web console. Installing Container Image The second path you can take is to install Prometheus using a container image. This lab path is provided using an Open Container Initiative (OCI) standards-compliant tool known as Podman. The default requirement will be to use Podman Desktop, a graphical tool that also includes the command line tooling referred to in the rest of this lab. I've chosen to avoid the more complex issues of mounting a volume for your local configuration file to be made available to your running Prometheus container image. Instead, I am choosing to walk you through a few short steps to building your own local container image with your workshop configuration file. Once all of this is done, you are up and running with your Prometheus server just like in the previous section. The rest of this path covers the same as previously covered in the above section, where you explore all the basic information available to you through the Prometheus status pages through its web console. Missed Previous Labs? This is one lab in the more extensive free online workshop. Feel free to start from the very beginning of this workshop here if you missed anything previously: You can always proceed at your own pace and return any time you like as you work your way through this workshop. Just stop and later restart Perses to pick up where you left off. Coming Up Next I'll be taking you through the following lab in this workshop where you'll start learning about the Prometheus Query Language and how to gain insights into your collected metrics. Stay tuned for more hands-on material to help you with your cloud-native observability journey.

Monitoring is a small aspect of our operational needs; configuring, monitoring, and checking the configuration of tools such as Fluentd and Fluentbit can be a bit frustrating, particularly if we want to validate more advanced configuration that does more than simply lift log files and dump the content into a solution such as OpenSearch. Fluentd and Fluentbit provide us with some very powerful features that can make a real difference operationally. For example, the ability to identify specific log messages and send them to a notification service rather than waiting for the next log analysis cycle to be run by a log store like Splunk. If we want to test the configuration, we need to play log events in as if the system was really running, which means realistic logs at the right speed so we can make sure that our configuration prevents alerts or mail storms. The easiest way to do this is to either take a real log and copy the events into a new log file at the speed they occurred or create synthetic events and play them in at a realistic pace. This is what the open-source LogGenerator (aka LogSimulator) does. I created the LogGenerator a couple of years ago, having addressed the same challenges before and wanting something that would help demo Fluentd configurations for a book (Logging in Action with Fluentd, Kubernetes, and more). Why not simply copy the log file for the logging mechanism to read? Several reasons for this. For example, if you're logging framework can send the logs over the network without creating back pressure, then logs can be generated without being impacted by storage performance considerations. But there is nothing tangible to copy. If you want to simulate into your monitoring environment log events from a database, then this becomes even harder as the DB will store the logs internally. The other reason for this is that if you have alerting controls based on thresholds over time, you need the logs to be consumed at the correct pace. Just allowing logs to be ingested whole is not going to correctly exercise such time-based controls. Since then, I've seen similar needs to pump test events into other solutions, including OCI Queue and other Oracle Cloud services. The OCI service support has been implemented using a simple extensibility framework, so while I've focused on OCI, the same mechanism could be applied as easily to AWS' SQS, for example. A good practice for log handling is to treat each log entry as an event and think of log event handling as a specialized application of stream analytics. Given that the most common approach to streaming and stream analytics these days is based on Kafka, we're working on an adaptor for the LogSimulator that can send the events to a Kafka API point. We built the LogGenerator so it can be run as a script, so modifying it and extending its behavior is quick and easy. we started out with developing using Groovy on top of Java8, and if you want to create a Jar file, it will compile as Java. More recently, particularly with the extensions we've been working with, Java11 and its ability to run single file classes from the source. We've got plans to enhance the LogGenerator so we can inject OpenTelementry events into Fluentbit and other services. But we'd love to hear about other use cases see for this. For more on the utility: Read the posts on my blog See the documentation on GitHub

Application modernization has become a hot topic in recent years as organizations strive to improve their systems and stay ahead of the competition. From improved user experience to reduced costs and increased efficiency, there are many reasons companies consider modernizing their legacy systems. So, should you consider this investment? Let’s find out! What Is Application Modernization? Application modernization updates legacy systems to align with current market trends and technologies. This involves upgrading or replacing the underlying infrastructure, architecture, and technology stack to improve efficiency, security, and user experience. The aim is to improve performance, functionality, and overall user experience while reducing maintenance costs. Application modernization helps organizations remain competitive by keeping their applications current and relevant. This can be done through various methods, such as re-architecting, re-platforming, or refactoring, to enhance architecture, migrate to a new platform, or modify the code base. The primary goal is to provide organizations with an efficient, secure, and user-friendly application that supports their business objectives. The Need for Application Modernization If an outdated or legacy system is not aligned with the company’s objectives, it can pose several difficulties. It may lack security patch updates, making it vulnerable to viruses and bugs that can disrupt its functionality. Additionally, these systems may present security risks as they are no longer supported by the vendor or company. Maintaining such systems can also be costly for businesses. Benefits of Application Modernization Boosts Productivity Training software developers and teams to use legacy systems can be costly and time-consuming. Furthermore, some outdated applications are not capable of automating repetitive tasks or integrating new processes, leading to decreased productivity among engineering teams. Modernizing applications integrate different aspects of the development process into a unified ecosystem, enabling employees to work on multiple tasks at once and reducing the time it takes to bring products to market. Additionally, modernized applications come equipped with advanced features and tools that simplify operations and don’t require extensive training, leading to increased employee productivity. Reduces Operational Costs and Tech Debt The maintenance requirements of legacy applications make them an uneconomical solution for organizations. Furthermore, these applications are typically hosted in on-premise data centers that are expensive to maintain and often lack adequate documentation, making it challenging to implement new features. As a result, these applications accrue a high amount of technical debt. In contrast, modernizing an enterprise’s technology infrastructure enables the organization to leverage the capabilities of the private cloud to meet emerging digital business requirements. This eliminates the need for a separate data center, as cloud databases offer a cost-effective pay-as-you-go model where you only pay for the services you use. The implementation of DevOps practices can significantly improve operations and reduce costs through the optimization of CI/CD pipelines, more efficient release cycles, and continuous improvement across all development processes. Improves Business Agility Traditionally, developers were required to create monolithic environments to make any modifications or updates to the code, server, and configurations. However, with the advent of application modernization, it is no longer necessary to shut down servers or plan extensive release updates, as the application is divided into multiple individually managed workloads. An example of the benefits of app modernization can be seen in the rapid growth of Pinterest. The company successfully increased its user base from 50,000 to 17 million in just nine months by scaling its processing and storage activities on Amazon Web Services. Enhances Scalability In a rapidly changing technological landscape, it is imperative for businesses to continually upgrade to remain competitive and successful. To thrive and expand physically and technologically, it is necessary for organizations to adapt and evolve. However, legacy systems often make it difficult to introduce new features or functionality, which can hinder a business’s growth and competitiveness. This is why organizations require storage solutions that can accommodate changing requirements and enable them to scale as needed. With the scalability offered by cloud technology, businesses can dynamically add or reduce IT resources to accommodate changing workloads. Keeps Up With Latest Trends Modernization of legacy systems allows companies to integrate the latest technologies and features into their older systems, seamlessly merging necessary components. This also enables organizations to take advantage of cutting-edge technologies such as big data, machine learning, artificial intelligence, and cloud computing. In the realm of customer service, modernization enables the use of AI and machine learning through predictive analytics, including natural language processing for chatbots and voice and text analysis. This is particularly beneficial in an era where personalization is a key differentiator for businesses. Provides Better Support and Maintenance Legacy applications can become costly to maintain over time due to bugs and outdated code. Modernization offers a solution by migrating legacy logic and code to a new platform and aligning the application infrastructure with the latest technologies and trends. This enables easier source code changes, database migrations, and documentation writing, as well as leveraging containerization and orchestration to set the desired state for modern applications. The re-engineering approach employed in modernization includes data and coding restrictions to ensure system security and prevent vulnerabilities. As a result, modernization enables companies to update their legacy systems with the latest technology stack, aligned with their business goals, for easier support and maintenance. Improves User Experience Legacy modernization prioritizes user experience (UX) to meet the demands for flexible and engaging digital experiences in web and mobile applications. The process involves the redesign of the user-facing components to improve information access and the overall UX. This redesign incorporates visual elements such as icons, font style, size, and others to create a distinct and visually appealing interface, intending to enhance perception, understanding, navigation, and interaction with the system or application. Conclusion Modernizing applications can bring many benefits, including improved user experience, enhanced security, cost savings, and improved integration. Businesses should consider this investment to keep up with the competition and meet changing customer needs. To achieve the best results, it’s essential to work with a trustworthy provider with experience in the application modernization process.

Software testing is the process of verifying the working of the software system or application. In other words, it ensures the software application is bug-free and addresses the technical and user requirements. It not only focuses on finding bugs or errors in the software but also considers measures to enhance the software quality in terms of usability, accuracy, and efficiency. Suppose you are building a software application that involves writing a bunch of codes and fixing bugs. These are part of the Software Development Life Cycle that developers and testers follow. However, it is their accountability to check or verify the code in line with the requirements and performance of the application. But what happens when an application is riddled with bugs that impact its features and quality? How could this be eliminated? Here enters software testing, a highly crucial part of the Software Development Life Cycle. What Is Testing? Testing can be understood as the technique used to determine the quality of the application based on the Software Requirement Specification (SRS). However, it is not possible for testing to detect all defects in an application. Testing aims to find the reason behind the failure of an application so it can be resolved. Some of the critical points on testing include: Provide a comparison between the state and working of software applications against different test scenarios. Involves validation and execution of test scripts in different environments. Gives detailed test reports on the errors and bugs. Testing is essential, as it involves the risk of application failure without testing. Hence, an application cannot be deployed to the end user without software testing. Now, let us connect with software testing. Introduction to Software Testing Software testing is the process of assessing the functionality of a software application. It checks for errors or bugs by evaluating the execution of the software’s components to meet the needs of the expected outcome. It identifies the accuracy and appropriateness of the application by taking into account its key attributes. Some of those attributes include: Reliability Scalability Portability Reusability Authentic Practical Usability It tends to provide an autonomous perception of software while ensuring its quality and hence is marked as the goal of software testing. To accomplish this, you test the software from a single line of code, its block, or even when the application is completed. Therefore, software testing holds specific objectives that allow better integration into the Software Development Life Cycle. Software testing in the present market is gaining immense popularity. According to Global Market insight, the size of the software testing market in 2019 was $40 billion and is anticipated to surge at a CAGR of 6% in 2026. The increasing demand for automating software development allows a surge in industry share. Additionally, as per Global Market insight, the automation testing market size in 2022 was valued at USD 20 billion and is expected to increase by over 15% CAGR within 2032. Some of the underlying objectives that software testing holds are: Investigate the defects and ensure the product's performance as per the specification. Gives surety to the product that it meets the market standards. Solves any of the challenges or loopholes at the production stage. Eliminate future failure of the product. It can be said that software testing should be carried out with a systematic approach to find defects in software. We know that technology is advancing and things are getting digitized. It is now easy to access online bank accounts, shop online from home, and have endless options. However, what if these systems turn out to be malfunctioning? A single defect can cause substantial financial losses for the organization. This is the main reason for the tremendous rise of software testing and holding a solid grip on IT. It is quite a common situation where a product experiences some defect; however, design errors may cause trouble if they go unnoticed. Hence, testing software applications is needed to ensure software development meets user requirements. Have you wondered if the software is deployed with bugs embedded? It will make error detection very tough for the testers. The reason is that screening thousands of lines of code and then fixing the errors is a huge problem. It can also happen that fixing one error may unknowingly lead to the rise of another bug in the system. Thus, testing software applications is characterized as a very important and essential part of software development. It is recommended to include software testing in every stage of the Software Development Life Cycle. Let's sum up software testing from the below-given points: Testing software applications are needed to investigate the software's credibility. Ensures a bug-free system, which can lead to failure. Ensures software is related to the needs of the user. Ensures the final product is user-friendly. Software is developed by humans and is prone to error. Developing software with no defects is impossible without including software testing in the development cycle. In the below section, we will get more clarity on the need for software testing. Why Do You Need Software Testing? The occurrence of defects in software can be due to many reasons. But, the matter of fact is that not all defects could pose threats to the system. We can accomplish much with software testing to ensure the software's effectiveness and quality. For example, any severe defect can delay the timely release of the product, which can lead to financial loss. Testing software applications is needed as it lowers the overall cost of the software development cycle. If, in any case, software testing is not executed in the initial stage of software development, it may turn out to be highly expensive later. Monetary and human losses are the noted consequences, and history is full of such examples. In April 2015 Bloomberg terminal in London crashed because of a software malfunction. It affected the lives of more than 300,000 traders. Failure of the POS system caused the shutdown of 60% of Starbucks stores in the US. Software failure noted in the sensory airbag detector made Nissan cars recall over 1 million cars from the market. Such losses are based on the fact that tracking back to find the bug or defect is a huge challenge. As a result, software testing aids in preventing the emergence of such scenarios. Software testing is required because it ensures that the software works and looks exactly like the specifications. Therefore, it strengthens the market reputation of an organization.However, you should also know when software testing should be executed. Some specific needs for testing software applications are summarized in the following points: Help identify bugs within written code. Improvise product quality. Gain customer confidence. Cut huge costs. Optimize business. Speeds up software development. What to Analyze in Software Testing? Having good information and an understanding of the project's needs is something a tester should be aware of. A good idea about the real-time environment where the software will be executed allows the tester to perform software testing efficiently. Hence, it is equally crucial for them to know what needs to be tested to devise a testing strategy. Below are the aspects that require software testing: Modularity Efficiency Design Accessibility GUI Code User-friendliness Security In addressing the need for testing software applications, it is vital to get an idea of their significance. It will help you learn the criticality of testing software applications. Let us see its significance. Significance of Software Testing Software testing is significant in determining bug-free software. It involves validating the system components manually or using different test tools to analyze specific defects. Here, we highlighted crucial reasons why testing software application is essential: Ensure Product Quality It checks the quality of the product by following its requirements. It ensures the functionality of the product to give a flawless user experience. For example, if you plan to launch a web application, ensuring compatibility with different browsers, real devices, and operating systems is important. Here, software testing plays a crucial role in checking cross-browser compatibility using a cloud-based platform. Satisfying Customer Demand The main objective of software organizations is to ensure customer satisfaction with the software. For this, software testing is opted to offer a perfect user experience. It provides the user's trust and enhances the organization's reputation. Improve Development Process With the help of quality assurance, it is possible to search for different arrays of scenarios and errors to reproduce the error. For developers, it is easy to fix those errors in no time. Further, the testers work with the development team, accelerating the development process. The Addition of Features Becomes Easy It is often difficult to change the code of the application as lines of code are interlinked. Change in any one could affect the other and lead to a rise of bugs. However, software testing counteracts this issue as it will help you know the codebase's exact loophole. Thus, it helps the developer confidently add new software features. Define Software Performance Software testing maintains an application's performance by regularly checking for errors or bugs. If noted, it gets modified or corrected in real time. Ensure Security Software security is a priority of any organization. Breaking the application's security can lead to a breach of data and the risk of being in huge loss. Poorly tested applications could increase the risk of the vulnerability of being hacked. Therefore, users prefer well-tested applications as they guarantee their data's security. Allows Saving Cost It ensures the project's cost-effectiveness by allowing early detection of errors or bugs. Such bugs or errors can be easily rectified at an early phase at a very low cost. Therefore, it is important to get testing done without any delay. Core Concepts of Software Testing Testing software applications may be problematic for some testers. To avoid the underlying issues, like difficulty in identifying bugs, can be overcome by knowing the fundamental concepts. When you start testing, following the given fundamental concepts will help you along the way: Test Strategy Being a tester requires being effective with the test strategy, which lays out the picture of software testing. A test strategy will help you know what type of testing will be appropriate for your application. Further, it gives information on which test to execute, the required time, and the effort to make testing effective. Tip: If you are setting up your first test strategy, you should focus on features that are priorities for your application. Test Plan When starting testing, a test plan is a must. It is a comprehensive document that entails test strategies, estimation, deadline, objective, and resources to complete the testing process. You can take the testing plan as the path that will clarify your scope of testing, like what is tested, by whom, and for how long. Along with that, it also contains information on any dependencies. Tip: You have to regularly update the testing plan as you find new bugs in the software application. Test Cases Test cases are defined as the set of actions executed on a system to determine whether it satisfies the requirements and functions of the software. Hence, it is written at a pace similar to the program. For example, if you sign into an account for an application, it is expected that you open the home dashboard every time. To execute this test, incorporate this information as a test case. Tip: Always give value to the most critical part of the application when setting up test cases. Test Data Test data is significant in case you want to run tests on the data of real users. Some examples of test data include product orders, sets of names, and other pertinent information on the application. No developer wishes to delete or update a real set of data from a real user’s application. Hence, it is crucial to retain a set of test data that can be further modified to ensure each of the functions of the software application is effectively working. Tip: Test data development should be done concurrently with test case development. Test Environment Testing software applications are equally important as the test environment in which it is performed. It is crucial to conduct tests on different devices, browsers, and OS to ensure software compatibility. For example, if you plan to execute performance or usability testing, you need to include various devices to execute the test on the application. Tip: Always set up a test environment before initiating testing. When to Perform Software Testing? Being a tester, you often try to avoid any complexity in testing software applications. It is highly preferred that the testing team starts software testing earlier, as it would allow the developer to finish the development process on time. Further, it will also take time and cost. However, if you start testing at a later stage of the software development process, it might delay the software release and can turn out to be expensive. The main reason is the challenge of keeping track of the changes or bugs for rectification once the software reaches its final release phase. Therefore, dividing the software development process into different phases is best. Then, perform testing in every such phase before heading forward to the next phase. It will allow you to quickly complete the software development process with adequate outcomes. Additionally, it enables the integration of diverse modules, as you will be aware that independent testing executed for every module is done and working according to the given specification. But you might think about how much time testing can be done. It just depends on the project’s needs. Also, software testing is not limited to any number. The frequency of software testing depends on how crucial the quality and security of the software application are to you and the organization. Preferably, testing needs to go in accordance with the development. In such a process, testers are accountable for finding a maximum number of defects in the initial phase of software development. It is essential because, for example, if any bug fixes or modifications are required in the software application's design, they can be incorporated early. Phases of Software Testing Each software undergoes different phases of the Software Testing Life Cycle (STLC). It is the sequence of actions performed during the testing process to meet the quality of the software. Detailed information on the different phases of software testing includes the following: Evaluation of Requirement It is the first phase in which the testing team identifies the testing requirements and items to be tested. The team defines such requirements (functional and non-functional) to check whether they are testable. Actions required in the requirement analysis phase: Recognize specific tests to be done. Collect details on testing priorities and focus. Develop a Requirement Traceability Matrix (RTM). Recognize the test environment. Analysis of automation feasibility. Test Planning In the next phase, the testing team prepares a plan that defines the project's time, cost, and effort. In addition to this, factors like the test environment, test limitations, test schedule, and resources are also determined. It includes the following elements: Developing a test plan and strategy Selection of testing tools Estimation of efforts Training needs Test Case Design Moving on to the test plan, testers work on writing and creating test cases. Identification of test data is followed by reviewing and reworking the test scripts and test cases. It includes the following action: Create test cases Review of test cases Create test data Setup of the Test Environment In the next phase, the testers determine the workings of the hardware and software of the products. It is usually done along with the test case development phase. Here, testers can do smoke testing for the given environment. The following activities are included in this phase: Comprehend the environment set-up and architecture. Prepare test data and test environment. Conduct smoke tests on the build. Test Execution During the test execution phase, testers evaluate the software they created using test plans and test cases. It is done for test script maintenance, bug reporting, and test script execution. The included activities are: Run the test as per the test plan. Document test outcome. Locate failed test cases. Retest the failed cases. Test Closure It is the last phase of testing. It includes an effective exchange of information on testing artifacts to recognize strategies that can be executed in the future. The following activities are involved in the test closure phase: Test completion reporting. Collection of test completion metrics. Develop test closure reports. Reporting of quality of products. Analysis of test results. Types of Software Testing In this section, we will discuss and go over various types of software testing in depth. In the software development cycle, the testers are often required to validate the software at different levels. You must be aware of some common testing types, like functional testing, Agile testing, and others. To that end, various types of software testing provide a framework whose ultimate goal is to ensure the software application is bug-free. Let's look at each type of testing. Software testing is broadly classified as functional testing and non-functional testing. To get a better idea, look at the chart below. Functional Testing This type of testing considers the functional requirements of an application. It involves various actions and functions of the system for testing. This is achieved by providing input and comparing the actual output to the expected output. Here, the test cases prepared are according to the requirements of the software and the customer. Generally, functional testing allows for the following checks: Testers should know the application's functionality. Always include the right set of data. The functional requirement of the application must align with the test data of the output application. All the test scenarios should be included. For the provided output, the result should be screened and recorded against the expected output. Different types of functional testing include the following: Unit Testing Testing done on specific units and components of software can be understood as unit testing. Here, the verification of the individual units or part of the source code is tested. It is mainly executed at the early stage of software development. A unit test case can be like clicking a button on a web page and validating that it works as expected. It is seen as a function, procedure, or method. For example, in unit testing, you will test the work of the login button to ensure it can route to the correct page link. To accomplish this, the developer mainly relies on automation testing tools for executing tests. Unit testing includes two major types: White Box Testing: It is also known as glass box or transparent testing. In white box testing technique, you test the internal structure or its underlying code base of the application. With this, it is easy to find any defect or error in the application's design. It follows techniques like data flow testing, path testing, decision coverage, and control flow testing. Gorilla Testing: In this type of testing, inputs are repeatedly applied on a module to ensure appropriate functioning and bug-free application. It considers every piece of code and tests using random input unless it crashes. It checks the robustness of the application as it involves every module in the software application. Hence, due to its nature, it is also known as fault tolerance or torture testing Integration Testing In this testing, different unit testing types are integrated to test the system as a whole. In other words, the application’s two or more two modules are integrated and tested. The main aim of integration testing is to find bugs in the interface, data flow, and interaction among the modules. The testers investigate how different units associate and give output for different scenarios. With the help of integration testing, errors about performance, requirements, and functional level are investigated. In unit testing, individual units are tested to know their performance as per expectation; however, in integration testing, such units' performance is checked when they are integrated. Integration testing is mainly classified into three types: Big Bang: Here, all the application modules are integrated and combined to establish a complete system. Followed to this, testing for the bug is done. Top-Down Approach: Initially, the top level of the module is tested, and then the addition of the sub-module is followed by its testing. Bottom-Up Approach: It is contrary to the top-down approach. In such testing, a test for the lowermost modules is done. Followed to this, a stepwise approach is applied to add and test high-level modules. System Testing In system testing, all the integrated modules of the complete system are tested. It enables testers to verify and validate whether or not the system's requirements are met. It involves a different test that includes validating output in terms of particular input and the user's experience. Here, performance and quality standards are tested in compliance with the technical and functional specifications. System testing is highly crucial when the system is deployed as it allows to development of a scenario resembling a real-time scenario. Hence, testing is mainly executed to investigate the behavior of the application, the architecture of the application, and the design of the software. It includes many different software testing categories, which tend to verify the whole system. Some of those system tests include the following: End-to-End (E2E) Testing As the name suggests, end-to-end testing involves verification and validation of the workflow of the software applications from start to end. This type of testing aims to mimic or stimulate the real user scenario to verify the system for data integrity and integration. The main aim of end-to-end testing is to check the application as a whole for its dependencies, communication, data integrity, and interfaces to perform a complete production-like environment. Black Box Testing It is the testing method where the software application's internal code structure is unknown, and testing is done to verify its functionalities. Therefore, the main root of information in black box testing is the requirement specified by the customer. The QA team chooses a specific function and provides input value to validate its functionality in the software. It verifies whether the function provides an anticipated output or not. If the function does not provide the correct output, the testing is marked as failed; otherwise, it passes. Smoke Testing Smoke testing is also known as build verification testing. It aims to validate whether the basic and complex function of the system is working as expected. In other words, it helps to determine whether to build provided by the developers for deployed software pertains to stability. It ascertains to explain the significant function of the program; however, it does not investigate its finer details. Sanity Testing It is part of regression testing, where testers perform sanity testing to check whether the code changes done in the software are working or functioning as expected. Sanity testing is done in the system to validate that new changes, newly added functions, or the fixation of bugs do not lead to failure. Hence, the tester performs sanity testing to ensure a stable build. Happy Path Testing It is also known as the golden path or sunny day testing. Happy path testing utilizes input and provides expected output. It tests the software application on a positive flow and does not consider errors or negative factors. The main focus area of this test is on positive input using, from which the application forms the expected output. Monkey Testing It is one of the types of system testing where random inputs of the application are provided by testers irrespective of any of the test cases to validate its behavior. Its main objective is to find new bugs to streamline the function of the application. The tester performs the test randomly without understanding the application's code. Hence, it is called monkey testing. Acceptance Testing In software application testing, on completion of unit, integration, and system testing, the next step is ensuring the application's quality. The QA team runs the test to define the quality in terms of predefined test cases and scenarios. In acceptance testing, the QA team investigates the whole system from the design view to its internal functions. It is marked to be very crucial in testing software apps as it considers the contractual and legal requirements of the application defined by the clients. Acceptance testing includes many different types of testing. Some of the included types are explained below: Alpha Testing The quality and engineering aspects of the developed software are considered in alpha testing. In other terms, it tests the product in line with the business requirement. Hence, it allows ensuring the successful working of the product. Beta Testing It is performed at the end stage of the Software Development Life Cycle after completing the alpha testing. Beta testing is conducted in the real environment before the deployment of the product. Hence, it gives a report on the fact that there is no major software failure, and it satisfies the end user's needs. User Acceptance Testing Before deploying the software application, the users test it. In user acceptance testing, specific requirements of the software application users use are primarily chosen for testing. Hence, it is also known as "end-user testing.". Operational Acceptance Testing This ensures the operational working of the processes and procedures are aligned in the system. It determines the software's recovery, reliability, maintainability, and compatibility before its release. Regulation Acceptance Testing It is also known as Compliance Acceptance Testing. It considers whether the developed software conforms with the regulations. In other words, it mainly tests the software against the defined rules and norms the government sets before the release. Regression Testing Regression testing is considered part of functional testing as it validates the intended function of the software. Due to the new changes required in software development, developers often make some enhancements or code fixes. However, such changes may impact the other functionality of the application. Regression testing ensures that new changes do not hamper or affect the existing features or give rise to a new bug in the application. It can be performed using some automation testing tools like Watir and Selenium. It includes the re-execution of a suite of test cases that were used in previously passed tests. Non-Functional Testing Non-functional testing is a type of testing that considers the non-functional aspect of the software like performance, usability, reliability, portability, efficiency, security, and others. It takes into account the behavior of the system and the end-user experience. Such testing is mainly measurable and helps lower the production risk and monetary value. The parameters of non-functional testing are presented in the below illustration. Non-functional testing holds different methodologies to perform testing. Some of them are as follows: Performance Testing It ensures the performance goals of the software application, like response time and throughput. With performance testing, the factors influencing software application performance, such as network latency, database transaction processing, data rendering, and load balancing between servers, are revealed in testing. It is mainly executed using tools like LoadRunner, JMeter, Loader, and others. The types of software application testing under performance testing are highlighted below: Load Testing It ensures the stability of the software application with the application of the load, which can be equal to or less than the intended number of their users. For example, your software application manages 250 users simultaneously with a noted response time of three seconds. In such a situation, load testing is conducted by applying a load of up to 250 or less 250. The main aim is to validate three seconds of response time. Stress Testing It ensures the application's stability and response time by giving a load of a number above the intended number of users of the application. For example, an application is designed to manage 5000 users simultaneously with a response time of five seconds. Stress testing is performed by applying a load of 5000 and above users. Followed by noticing the response time. Scalability Testing It checks the application's scalability by applying a load of more than the designed number and investigates the point where the application can crash. It is done to confirm the ability of software to be scaled up and down to align the changes done. For example, if an application handles 3000 users at a time and the response time is five seconds. Then, scalability testing is conducted with a load of more than 3000 users. Gradually, the load value increases to figure out the exact point at which the application can crash. Flood Testing A large volume or set of data is transferred to the database to check the stability and response time of the testing. In other words, the QA team uses flood testing to investigate the capacity of the database to manage the data. Endurance Testing It is executed to ensure that a software application or the system can handle the continuous load over the long term. In other words, it ensures that the application works properly for a more extended period. Usability Testing In simple terms, the tester tests the application's working in terms of user-friendliness. It is also categorized under black box testing, and application tests ensure easy utilization of the user interface by the user. Testing is done in three aspects: convenience, usability, and learning ability. Usability testing is done to ensure the quality and easiness of application usage. It can be explained with an example. A gaming application’s usability testing checks whether it is operated by both hands, the color of the background, the vertical scroll, and others. The type of usability testing includes the following: Cross-Browser Testing: It involves testing the application on various browsers, operating systems, and mobile devices. Cross-browser testing ensures the application's compatibility, as it should work on all different browsers, mobile devices, and operating systems. Accessibility Testing:This type of testing help defines the accessibility of the software for people with impairments. For example, in accessibility testing, various checks are done, like font size and color for visually impaired people and color blindness. Exploratory Testing: It is considered informal testing to identify the application and address the existing defect. Based on business domain knowledge, exploratory testing validates the application. Security Testing Software testing involves tests for security that unmask the software application's risks, threats, and vulnerabilities. It intends to prevent malicious attacks and identify loopholes in the software system. It involves two crucial aspects of testing-authentication and authorization. Security testing is an important aspect, as it makes the application secure and able to store confidential information when required. It also checks the behavior of the software related to attacks from hackers and how it should be maintained for data security upon noticing such attacks. Security testing is of different types, including the following: Penetration Testing: Pen testing evaluates the system to validate for vulnerabilities to external hacking attempts. This test is mainly executed by the authorized cyberattack on the system to figure out the limitation of the system with respect to security. The operations like SQL injection, Privilege Elevation, session expiry, and URL manipulation are performed to execute security testing. Vulnerability Scanning: It is executed using automated software to scan or screen the system on the ground of vulnerability signatures. Security Auditing: It includes the internal inspection of applications and OS for security limitations. Further, an inspection of code line wise is also conducted to perform the audit. Security Scanning: It is mainly done to find system and network weaknesses and then provide answers for lowering associated risks. Ethical Hacking: It includes hacking an organization's software system. Here, the primary intention is to uncover the security flaws in the system. Portability Testing:This type of testing is performed to test how changes in the environment can lead to modification in the performance of the software. For example, it checks how the software operates in different versions of OS or web browsers.Basically, such testing is done if the customer proposes utilizing software applications on multiple platforms. However, it can also be regarded as the subset of system testing and is mainly executed after integration testing. Other Types of Software Testing Software testing involves various approaches to ensure the application's quality, performance, security, and functionality. Some other types of testing known in the successful development of software applications are briefly highlighted below: Graphical User Interface (GUI) Testing GUI testing is performed to test whether the graphical user interface of the software application is working appropriately as per the requirement. It checks the functionality as well as defines its adherence to quality standards. Some of the common aspects tested in GUI testing are: Layout Labels Captions Icons Links Content Buttons Lists Non-Graphical User Interface Testing The testing of anything other than a graphical user interface comes under non-graphical user interface testing. For example, it tests the command line interfaces, batch process, and other events, instigating specific use cases in the application. Mutation Testing It is the category of white box testing and is executed by modifying the source code of an application and then validating whether existing test cases can recognize such defects in the system. Since the changes done are minimal, it does not impact the functionality of the applications. Risk-Based Testing The functionality is tested on the ground of the priority of the business and is more prone to failure. It is done by setting the priority for all functionality. Followed to this, the high-priority test cases are executed, proceeding with medium and low-priority functionality. Noting the different types of testing, it is equally important to know about the software testing approaches. This is explained below in the given section. Approaches to Software Testing Software testing includes endless types, and each of those is performed using different approaches. Basically, the testing approach can be understood as the method or strategy utilized to execute the test systematically. Manual and automation testing approaches are commonly used methods to execute software testing. Each approach has specific requirements and purposes at the diverse stage of the software development cycle. Let us look into this in more detail. Manual Software Testing Manual testing involves the execution of testing without the use of any automated tools. While testing, all the test cases are performed by the tester manually as per the end user’s view. Some crucial aspects of manual testing involve: Able to find hidden and visible bugs in the software. Perform manual testing before executing automated testing. Although it involves a considerable amount of time and effort but provides a bug-free application. Able to uncover human-related errors like user interface problems. Since it involves human input, it is prone to error. Manual testing includes three major types, explained in the previous section: White Box Testing Black Box Testing Gray Box Testing How to Perform It? All the different types of manual testing follow a set of steps. Below are the steps required while performing manual testing: Evaluate the needs of software project documentation and guides. Form a test plan. Covering all the test requirements specified in the document, test cases are written. The QA lead then reviews the test cases. On approval, test cases are executed, and bugs are detected. On finding bugs, report them, and then fix them by re-running the failed test cases. In a nutshell, manual testing is impossible to avoid in software testing because it is a continuous process. It needs human verifications regularly. However, this demands an equal balance between automation testing. Even though the agile approach toward software development is inclined towards automation testing, the manual approach is required. Now let us see what does automation testing do? Automated Software Testing Automation testing is an approach to automate web or mobile app tests using automated testing tools and scripts. In other words, automation tools automatically run tasks in a pre-defined pattern. It is preferred by the tester to run software testing due to the following reasons: Automated testing increases test coverage, effectiveness, and test speed. Able to identify human-related errors. Save time in testing. Gives more control over the testing process. However, you might think, when should we do automated software testing? Let's see this with an example. In regression testing, we test the changed code and its impact on other software application functionality. Using manual testing in this approach seems to waste time and effort. The main reason is the need to perform a complete application test again. Therefore, rather than expecting humans to repeat the same test with similar speed, energy, and speed, it is preferred and logical to use software tools to execute such tests. Here, automation testing comes in. When starting with automation testing, you should consider that not every test can be automated. Some examples of a test that uses the automated software testing approach include: Smoke testing Data-driven testing Performance testing Functional testing Regression testing Unit testing Security testing While performing automated testing, a certain set of steps requires to be followed to get accurate and fast output. Read the section below on how to execute automated software testing. How to Perform It? While moving from manual to automation testing, it is crucial to consider the realistic goal by setting smaller test cases, addressing aspects not requiring automation, and others. However, several crucial stages of automated testing include the following, which run parallel with the software development life cycle. Defining the scope: To overcome any future challenges, the goal and objective of automation testing need to be penned down. It should also include budget and resources. Select the right automation testing tools: To perform automated testing; tools are the prerequisite factor. However, choosing the right automation tools depends on the web application’s technology being tested. Some of the tools include Selenium, Cypress, Playwright, Appium, etc. Creating a test script: Write automated test scripts which mimic the activities of users and verify the behavior of the software. Test environment set-up: The test environment, including software, hardware, and data, requires to be set up for test execution. Execute test: Then run the automated test by monitoring the output. Debug and maintenance: Noticing bugs and errors during the test, debugging, and maintaining the automated test scripts. Continuous integration: Automation testing requires to be integrated into the software development process. The main reason is executing the test every time on changing the code. While performing automated testing, selecting the right testing platform is crucial for more reliable and scalable test automation. It is understood as the infrastructure where all automated tests run. It includes different versions of devices, OS, and browsers on which software testing is executed. It also provides parallel testing, where multiple tests on multiple devices run simultaneously. As we discuss the different approaches to testing, specific metrics are used to check and determine the testing quality, like defect metrics, defect severity, etc. Let's get into this in detail. Software Testing Metrics The primary focus of testing is to deliver high-quality software applications. To accomplish this, software testing metrics are considered to measure and quantify the testing process. Some metrics against which testing is executed are explained below: Test Coverage: It takes into account the extent to which the application is tested and evaluated in terms of functionality, need, and code coverage. Base Metrics: QA team collects the test data during test cases' development and execution. The test reports are generated, and the metric is shared with test leads and managers. It considers the total number of test cases and test cases completed. Calculated Metrics: Calculated metrics are created from base metrics data. Such metrics are used by testers to track the development of software applications and their progress. Defect Metrics: These give information on the aspects of software quality, like functionality, usability, compatibility, and installation stability. Defect Severity: Help see how defects can affect the quality of the software application. Test Case Efficiency: It measures how effectively test cases detect bugs. Defect Density: It is the number of confirmed bugs in a software application during the development period, divided by the software application size. Such metrics allow tracking and monitoring of the quality of the testing approach over time. Along with this, it helps identify areas of improvement and decide how to allocate resources, and prioritize efforts. Let us look at the different strategies used for software testing. Strategies for Software Testing In software testing projects, two crucial factors that require consideration are strategy and investment. Here, strategy is a priority as it gives information on techniques and tools needed for testing. Some of those strategies which can be used are explained below: Static Testing Strategy The test where the developer reviews the code before pushing is a form of static test. Here, the system's quality is evaluated without running the system. It allows early bug detection and fixation. Structural Testing Strategy It is one of the strategies under unit testing, where the system is tested as a whole and validated on real devices to find all bugs. It may be called white box testing as it is executed on different components and interfaces to find defects in data flows. Behavioral Testing Strategy It addresses the system's working in terms of performance, configuration, workflow, etc. The main focus is to test web applications or websites as per the user’s view, hence called the black box test. It can be done through both manual and automated testing approaches. However, having different testing strategies, you need to know the basis for selecting appropriate software testing approaches. You can consider the following factors: Associated risk during the testing. Requirement of the stakeholders. Regulation of the organization. Additionally, software testing strategies mainly focus on the recognition of bugs. The best approach to detecting all bugs is to run the application on real devices and browsers. However, manual and automated testing should be the central area for testing a website or web application. Automated testing should complement manual testing to detect all the bugs. Software Testing Tools Software testing is made easier with the availability of testing tools. They support various test activities from planning, gathering, building creation, test execution, and analysis. For the automated testing approach, different ranges of tools are available, which can be used as per the needs. Let's get into details about such tools categorized under automation testing tools. Some of the most trending automation testing tools for software testing are explained below: Selenium Selenium is an open-source automated testing tool that tests web applications across different browsers and operating systems. It is one of the leading automation testing frameworks for your web testing requirements. If you are testing an application in a browser and want to expedite the process, you can automate the testing process with Selenium. Cypress Cypress is one of the popular front-end automation testing tools for the modern web. It is based on JavaScript and uses DOM manipulation techniques directly in the browsers. You can write unit tests, end-to-end tests, and integration tests with Cypress. It does not necessitate adding explicit or implicit wait commands. Playwright Playwright is an automation testing framework based on the Node.js library and another chosen favorite for a larger audience. It automates Chromium, WebKit, and Firefox using a single API. Playwright supports multiple programming languages like C#, Java, Python, and Node.js. Puppeteer Puppeteer is a Node.js library and allows headless browser testing for Google Chrome. It uses commands in JavaScript that allows performing test actions on Chrome browsers. Puppeteer offers high-level API in situations where control of Chromium or Headless Chrome is needed with DevTools Protocols. Appium Appium is an open-source tool for mobile automation testing for Android and iOS apps. It also works for hybrid apps. Appium can be used for automated functional testing that improves the overall functionality of mobile applications. Overall, there are an endless number of tools available for easy automation. You must only cross-check your business requirements to choose the appropriate testing tools. Where to Test Software Applications? Testing ecosystems contain various techniques that testers can choose from depending on their requirements. Testing is intended to ensure that the application under test is reliable, secure, and safe. There are two methods to perform software application testing: On-premise testing Cloud testing On-Premise Testing On-premise testing involves testing software applications on local machines, systems, or devices in an office. As a result, it entails a great deal of responsibility. Maintenance, monitoring, and upgrading the machines and software, and installing and upgrading - you'll need everyone on board. Furthermore, on-premise testing is quite expensive and time-consuming. Cloud Testing Cloud testing evaluates web applications (or websites) for scalability, performance, security, and reliability. This testing is performed on a cloud computing environment hosted by a third party, which houses the necessary infrastructure. There is a lot of talk about transforming businesses digitally. For those who want to embrace digital transformation, it's recommended to choose cloud-based testing over on-premise testing. There are numerous benefits of cloud testing. It is here to stay so you can keep ahead of the curve with the least operational overhead. How to Perform Software Testing on the Cloud? Cloud testing is effective for web and mobile application testing without worrying about the local test infrastructure. Testing on the cloud helps you leverage different browsers, devices, and various operating systems and eliminate coverage limitations for OS, devices, and browsers. You can perform manual and automated web and mobile application testing with cloud-based platforms. It allows you to perform real-time testing, real-device testing, and automation testing on various browsers, browser versions, devices, and operating systems. Challenges and Solutions in Software Testing At present, software application testing is reinforced with regular updates to meet the changes in user expectations and technology. The rise of the Agile approach made it easy for testers to update the software at lightning speed. However, it brings a few challenges to software testing. Let’s shed some light on those challenges and their solutions. Challenge 1: Inadequate Communication Poor communication within the team regarding the development of correct test cases can be one of the challenges. Considering that the right test cases cannot be prepared unless they know the business and technical needs is crucial. Solution: Always collaborate with the testing and development teams while doing software testing. Challenge 2: Difference in Testing Environment Development of software requires working effortlessly across diverse device and browser platform combinations. However, the availability of thousands of mobile devices on the market created a significant challenge in software application testing. Although emulators and simulators are one option for overcoming this, they do not confirm the application's primary functionality in real-world user scenarios. Solution: A cloud-based testing platform, such as LambdaTest, provides real-device cloud access, allowing users to test across 3000+ real browsers and devices. They are also integrated with automation test tools and frameworks that ease software testing. In addition to the above challenges, software testing is often misunderstood as a debugging method by some new testers who often mistake software application testing as a debugging method. However, both are very different in their meaning. Learn in detail about the major difference between software testing and software debugging to have a clear understanding. Software Testing vs. Software Debugging Novice testers often get confused with the terms "Software Testing" and "Software Debugging." They may sound similar, but they are different. Some of the key differences could be understood from the below-given points. Software Testing Software Debugging It should be done throughout the Software Development Life Cycle. Software debugging is done when software application testing gets over. It unmasks the defects. Debugs remove the identified defects by locating them. It is part of the software development cycle. Debugging is part of the testing process. It initiates as soon as the development of software begins. Debugging initiates when the testers report any defects. Verification and validation of software are involved in testing. In debugging, the real cause behind the bugs is rectified. Best Practices for Software Testing To ensure the perfect execution of software testing and deliver a high-quality product, it is vital to incorporate its best practices. The following are the best practices: Before testing begins, planning and preparing the test by defining the scope, identifying test objectives, and selecting appropriate tools is crucial. Test cases should be defined clearly and comprehensively based on all aspects of the software. Further, it is crucial to note that test cases should be associated with test objectives. Make use of automation testing tools to run repetitive and time-consuming tests. Initiate the testing process early in the software development process. Continued execution of the testing process throughout the entire development lifecycle is recommended. Perform testing on multiple platforms and devices to confirm that it works with all test environments. Always have thorough tests using different testing techniques like unit testing, functional testing, integration testing, and performance testing. Work in close association with the development team. It helps ensure testing integration into the development process and the timely fixation of bugs. Conclusion The detailed discussion on software testing would help gain a good understanding of its concepts. It has been an essential part of the software development process. It ensures the quality, performance, and reliability of software. It can be said that with a continuous process of analyzing and improving testing, the organization provides its updated practice of software testing in line with the latest industry standards and best practices. Finally, combining manual and automated testing methods and practices such as early and frequent testing and team collaboration allows for the detection and timely correction of bugs.

Web application testing is an essential part of the software development lifecycle, ensuring that the application functions correctly and meets the necessary quality standards. Best practices for web application testing are critical to ensure that the testing process is efficient, effective, and delivers high-quality results. These practices cover a range of areas, including test planning, execution, automation, security, and performance. Adhering to best practices can help improve the quality of the web application, reduce the risk of defects, and ensure that the application is thoroughly tested before it is released to users. By following these practices, testing teams can improve the efficiency and effectiveness of the testing process, delivering high-quality web applications to users. 1. Test Early and Often Testing early and often means starting testing activities as soon as possible in the development process and continuously testing throughout the development lifecycle. This approach allows for issues to be identified and addressed early on, reducing the risk of defects making their way into production. Some benefits of testing early and often include: Identifying issues early in the development process, reducing the cost and time required to fix them. Ensuring that issues are caught before they impact users. Improving the overall quality of the application by catching defects early. Reducing the likelihood of rework or missed deadlines due to last-minute defects. Improving collaboration between developers and testers by identifying issues early on and resolving them together. By testing early and often, teams can ensure that the web application is thoroughly tested and meets the necessary quality standards before it is released to users. 2. Create a Comprehensive Test Plan Creating a comprehensive test plan involves developing a detailed document that outlines the approach, scope, and schedule of the testing activities for the web application. A comprehensive test plan typically includes the following elements: Objectives: Define the purpose of the testing and what needs to be achieved through the testing activities. Scope: Define what functionalities of the application will be tested and what won't be tested. Test Strategy: Define the overall approach to testing, including the types of testing to be performed (functional, security, performance, etc.), testing methods, and tools to be used. Test Schedule: Define the testing timelines, including the start and end dates, and the estimated time required for each testing activity. Test Cases: Define the specific test cases to be executed, including input values, expected outputs, and pass/fail criteria. Environment Setup: Define the necessary hardware, software, and network configurations required for testing. Test Data: Define the necessary data required for testing, including user profiles, input values, and test scenarios. Risks and Issues: Define the potential risks and issues that may arise during testing and how they will be managed. Reporting: Define how the testing results will be recorded, reported, and communicated to stakeholders. Roles and Responsibilities: Define the roles and responsibilities of the testing team and other stakeholders involved in the testing activities. A comprehensive test plan helps ensure that all testing activities are planned, executed, and documented effectively, and that the web application is thoroughly tested before it is released to users. 3. Test Across Multiple Browsers and Devices Testing across multiple browsers and devices is a crucial best practice for web application testing, as it ensures that the application works correctly on different platforms, including different operating systems, browsers, and mobile devices. This practice involves executing testing activities on a range of popular web browsers, such as Chrome, Firefox, Safari, and Edge, and on various devices, such as desktops, laptops, tablets, and smartphones. Testing across multiple browsers and devices helps identify issues related to compatibility, responsiveness, and user experience. By testing across multiple browsers and devices, testing teams can: Ensure that the web application is accessible to a wider audience, regardless of their preferred platform or device. Identify issues related to cross-browser compatibility, such as variations in rendering, layout, or functionality. Identify issues related to responsiveness and user experience, such as issues with touchscreens or mobile-specific features. Improve the overall quality of the application by identifying and resolving defects that could impact users on different platforms. Provide a consistent user experience across all platforms and devices. In summary, testing across multiple browsers and devices is a crucial best practice for web application testing, helping ensure that the application functions correctly and delivers a high-quality user experience to users on all platforms. 4. Conduct User Acceptance Testing (UAT) User acceptance testing (UAT) is a best practice for web application testing that involves testing the application from the perspective of end-users to ensure that it meets their requirements and expectations. UAT is typically conducted by a group of users who represent the target audience for the web application, and who are asked to perform various tasks using the application. The testing team observes the users' interactions with the application and collects feedback on the application's usability, functionality, and overall user experience. By conducting UAT, testing teams can: Ensure that the application meets the requirements and expectations of end-users. Identify usability and functionality issues that may have been missed during other testing activities. Collect feedback from end-users that can be used to improve the overall quality of the application. Improve the overall user experience by incorporating user feedback into the application's design. Increase user satisfaction by ensuring that the application meets their needs and expectations. UAT is an essential best practice for web application testing, as it ensures that the application meets the needs and expectations of end-users and delivers a high-quality user experience. 5. Automate Testing Automating testing is a best practice for web application testing that involves using software tools and scripts to execute testing activities automatically. This approach is particularly useful for repetitive and time-consuming testing tasks, such as regression testing, where automated tests can be executed quickly and efficiently. Automation testing can also help improve the accuracy and consistency of testing results, reducing the risk of human error. By automating testing, testing teams can: Reduce testing time and effort, allowing more comprehensive testing to be performed within the available time frame. Increase testing accuracy and consistency, reducing the risk of human error and ensuring that tests are executed consistently across different environments. Improve testing coverage by allowing for more tests to be executed in a shorter time frame, increasing the overall effectiveness of the testing process. Facilitate continuous testing by enabling automated tests to be executed automatically as part of the development process, allowing issues to be identified and resolved more quickly. Reduce testing costs by reducing the need for manual testing and increasing testing efficiency. Automating testing is an essential best practice for web application testing, as it can significantly improve the efficiency and effectiveness of the testing process, reduce costs, and improve the overall quality of the application. 6. Test for Security Testing for security is a best practice for web application testing that involves identifying and addressing security vulnerabilities in the application. This practice involves conducting various testing activities, such as penetration testing, vulnerability scanning, and code analysis, to identify potential security risks and vulnerabilities. By testing for security, testing teams can: Identify and address potential security vulnerabilities in the application, reducing the risk of security breaches and data theft. Ensure compliance with industry standards and regulations, such as PCI DSS, HIPAA, or GDPR, that require specific security controls and measures to be implemented. Improve user confidence in the application by demonstrating that security is a top priority and that measures have been taken to protect user data and privacy. Enhance the overall quality of the application by reducing the risk of security-related defects that could impact users' experience and trust in the application. Provide a secure and reliable platform for users to perform their tasks and transactions, improving customer satisfaction and loyalty. Testing for security is a critical best practice for web application testing, as security breaches can have significant consequences for both users and businesses. By identifying and addressing potential security vulnerabilities, testing teams can ensure that the application provides a secure and reliable platform for users to perform their tasks and transactions, reducing the risk of security incidents and data breaches. 7. Perform Load and Performance Testing Load and performance testing are best practices for web application testing that involve testing the application's ability to perform under various load and stress conditions. Load testing involves simulating a high volume of user traffic to test the application's scalability and performance, while performance testing involves measuring the application's response time and resource usage under different conditions. By performing load and performance testing, testing teams can: Identify potential bottlenecks and performance issues that could impact the application's usability and user experience. Ensure that the application can handle expected traffic loads and usage patterns without degrading performance or causing errors. Optimize the application's performance by identifying and addressing performance issues before they impact users. Improve user satisfaction by ensuring that the application is responsive and performs well under various conditions. Reduce the risk of system failures and downtime by identifying and addressing performance issues before they cause significant impacts. Load and performance testing are essential best practices for web application testing, as they help ensure that the application performs well under various conditions and user loads. By identifying and addressing performance issues, testing teams can optimize the application's performance, improve user satisfaction, and reduce the risk of system failures and downtime. 8. Conduct Regression Testing Regression testing is a best practice for web application testing that involves retesting previously tested functionality to ensure that changes or fixes to the application have not introduced new defects or issues. This practice is particularly important when changes have been made to the application, such as new features or bug fixes, to ensure that these changes have not impacted existing functionality. By conducting regression testing, testing teams can: Ensure that changes or fixes to the application have not introduced new defects or issues that could impact user experience or functionality. Verify that existing functionality continues to work as expected after changes have been made to the application. Reduce the risk of unexpected issues or defects in the application, improving user confidence and trust in the application. Improve the overall quality of the application by ensuring that changes or fixes do not negatively impact existing functionality. Facilitate continuous testing and delivery by ensuring that changes can be made to the application without introducing new issues or defects. Regression testing is an important best practice for web application testing, as it helps ensure that changes or fixes to the application do not negatively impact existing functionality. By identifying and addressing issues before they impact users, testing teams can improve the overall quality of the application and reduce the risk of unexpected issues or defects. 9. Document and Report Defects Documenting and reporting defects is a best practice for web application testing that involves tracking and reporting any issues or defects found during testing. This practice ensures that defects are documented, communicated, and addressed appropriately, improving the overall quality of the application and reducing the risk of user impact. By documenting and reporting defects, testing teams can: Ensure that all defects are tracked, documented, and communicated to the appropriate stakeholders. Prioritize and address critical defects quickly, reducing the risk of user impact and improving the overall quality of the application. Provide clear and detailed information about defects to developers and other stakeholders, improving the efficiency of the defect resolution process. Ensure that defects are resolved appropriately and that fixes are properly tested before being deployed to production. Analyze defect trends and patterns to identify areas of the application that require further testing or improvement. Documenting and reporting defects is a critical best practice for web application testing, as it ensures that defects are properly tracked, communicated, and addressed, improving the overall quality and reliability of the application. By identifying and addressing defects early in the development cycle, testing teams can reduce the risk of user impact and ensure that the application meets user requirements and expectations. 10. Collaborate With the Development Team Collaborating with the development team is a best practice for web application testing that involves establishing open communication and collaboration between the testing and development teams. This practice ensures that both teams work together to identify, address, and resolve issues and defects efficiently and effectively. By collaborating with the development team, testing teams can: Ensure that testing is integrated into the development process, improving the efficiency of the testing and development process. Identify defects and issues early in the development process, reducing the time and cost required to address them. Work with developers to reproduce defects and provide detailed information about issues, improving the efficiency of the defect resolution process. Identify areas of the application that require further testing or improvement, providing valuable feedback to the development team. Ensure that the application meets user requirements and expectations, improving user satisfaction and confidence in the application. Collaborating with the development team is an essential best practice for web application testing, as it ensures that both teams work together to identify, address, and resolve issues efficiently and effectively. By establishing open communication and collaboration, testing and development teams can ensure that the application meets user requirements and expectations while improving the efficiency of the testing and development process. Conclusion Web application testing is a critical process that ensures the quality, reliability, and security of web-based software. By following best practices such as proper planning, test automation, a suitable test environment, a variety of testing techniques, continuous testing, bug tracking, collaboration, and testing metrics, testers can effectively identify and fix issues before the software is released to the public, resulting in a better user experience.

Git is a version control system that has become an essential tool for developers worldwide. It allows developers to keep track of changes made to a project's codebase, collaborate with others on the same codebase, and roll back changes when necessary. Here are the top 11 Git commands every developer should know. 1. git config git config is a command that allows you to configure Git on your system. It enables you to view and modify Git's settings, such as your user name and email address, default text editor, and more. The git config command is used to set configuration values that affect the behavior of Git. Configuration values can be set globally or locally, depending on whether you want the configuration to apply to all Git repositories on your system or just the current repository. Some common use cases of the git config command includes setting your user name and email address, configuring the default text editor, and customizing Git's behavior. By using git config, you can tailor Git to your specific needs and preferences, making it easier and more efficient to work with Git on your projects. Setting your user name and email address globally: git config --global user.name "Riha Mervana" git config --global user.email "riha@youremail.com" You can read back these values as: git config --list Output: user.name=Riha Mervana user.email=riha@youremail.com When you open the global configuration file ~/.gitconfig, you will see the content saved as: [user] name = Riha Mervana email = riha@youremail.com 2. git init The first command every developer should know is git init. This command initializes an empty Git repository in the current directory. This command creates a .git directory in the current directory, which is where Git stores all the information about the repository, including the commit history and the files themselves. The git init command can be used in two ways: Either changes a directory using the cd command and run git init to create a Git repository…. git init Or create an empty Git repository by specifying a directory name using the git init command. git init <directory-name> 3. git clone git clone is used to create a local copy of a remote repository. This command downloads the entire repository and its history to your local machine. You can use this command to create a local copy of a repository that you want to contribute to or to start working on a new project. Here is an example of how HTTPS looks. git clone <https://github.com/reactplay/react-play.git> This will clone the react-play project locally for you. Then you can change to the directory and start working on it. cd react-play 4. git add git add is used to stage changes made to a file. This command tells Git that you want to include the changes made to a file in the next commit. You can add individual files or directories or all changes in the current directory by using the git add . command. The git add command is used to send your file changes to the staging area. git add <file-name> Also, git add <directory-name> 5. git commit git commit is used to save changes made to the repository. This command creates a new commit with a message that describes the changes made. The message should be descriptive and provide context about the changes made. git commit -m "add a meaningful commit message" 6. git push git push is used to upload local changes to a remote repository. This command sends the changes made in your local repository to the remote repository, where other developers can access them. You can use this command to contribute changes to an open-source project or to share changes with your team. git push <remote> <branch-name> 7. git pull git pull is used to download changes made to a remote repository to your local repository. This command is useful when you want to work on the latest version of a project or when you want to merge changes made by other developers into your local repository. git pull 8. git branch git branch is used to create, list, and delete branches. A branch is a copy of the repository that you can use to work on new features or fixes without affecting the main branch. You can use this command to create a new branch, list all the branches in the repository, or delete a branch. List all the branches: git branch Create a new branch with a branch name: git branch <branch-name> Delete a specific branch: git branch -d <branch-name> Rename a branch: git branch -m <branch-name> List all Remote branches (with a marking of the current branch): git branch -a 9. git merge git merge is used to merge changes made in one branch into another branch. This command is useful when you want to incorporate changes made in a feature branch into the main branch. You can use this command to merge changes made by other developers into your local branch or to merge your changes into the main branch. git merge <branch-name> 10. git checkout git checkout is used to switch between branches or to revert changes made to a file. This command allows you to move between branches or switch to a specific commit in the commit history. You can also use this command to discard changes made to a file and revert it to a previous state. git checkout <branch-name> 11. git log git log is used to view the commit history of a repository. This command displays a list of all the commits made to the repository, including the commit message, the author, and the date and time of the commit. You can use this command to track changes made to the repository over time and to identify which commits introduced specific changes. git log <options> <branch_name> Conclusion Git is a powerful version control system that is widely used in software development. Knowing how to use Git effectively is essential for developers to collaborate on projects, keep track of changes, and maintain code quality. These above commands provide developers with the basic tools they need to manage their codebase effectively. However, Git is a complex system with many additional features and commands that can be used to improve workflow and productivity. Therefore, developers should strive to learn more about Git and its capabilities in order to take full advantage of its benefits.