Testing, Deployment, and Maintenance

In this article, I present an example of a simple and quick installation of ScyllaDB in the AWS cloud using Terraform. Initially, I intended to create a ScyllaDB AMI image using HashiCorp Packer. However, I later discovered that official images are available, allowing ScyllaDB to be easily configured during instance initialization via user data. In fact, user data can define all parameters supported in scylla.yaml. Additional options and examples can be found in scylla-machine-image GitHub repository. What else should you know? For ScyllaDB to automatically configure and start, supported instance types must be used. A list of such instance types can be found here: ScyllaDB System Requirements for AWS. In our example, we’ll use the i4i.large type as it is the cheapest among supported types. Assumptions A single seed node is sufficient for the setup.Hosts are publicly accessible with restricted access from a specific IP address (a static public IP is required). Terraform Configuration Example Following best practices, the Terraform code is divided into multiple files in a single directory. Variables File (variables.tf) Plain Text variable "scylladb_version" { type = string default = "6.2.1" description = "The version of the ScyllaDB to install." } variable "your_public_network" { type = string default = "0.0.0.0/0" description = "Your public static IP address or your provider network." } variable "instance_type" { type = string default = "i4i.large" description = "The AWS instance type." } variable "number_of_regular_hosts" { type = number default = 2 description = "The number of the regular (not seed) hosts in a cluster." } variable "ssh_key_name" { type = string default = "my_ssh_key" description = "The name of your public SSH key uploaded to AWS." } This file contains the definition of variables used in the code contained in main.tf. We’ll discuss them later. Main Configuration File (main.tf) Plain Text terraform { required_providers { aws = { source = "hashicorp/aws" version = "~> 5.0" } } } # Configure the AWS Provider provider "aws" { region = "eu-west-1" } variable "scylladb_version" { type = string default = "6.2.1" description = "The version of the ScyllaDB to install." } variable "your_public_network" { type = string default = "0.0.0.0/0" description = "Your public static IP address or your provider network." } variable "instance_type" { type = string default = "i4i.large" description = "The AWS instance type." } variable "number_of_regular_hosts" { type = number default = 2 description = "The number of the regular (not seed) hosts in a cluster." } variable "ssh_key_name" { type = string default = "my_ssh_key" description = "The name of your public SSH key uploaded to AWS." } data "aws_ami" "scylladb_ami" { filter { name = "name" values = ["ScyllaDB ${var.scylladb_version}"] } } resource "aws_security_group" "scylladb_all" { name = "scylladb_all" description = "Will allow all inbound traffic from your public IP" tags = { Name = "ScyllaDB" } } resource "aws_vpc_security_group_ingress_rule" "allow_all_inbound_traffic_ipv4" { security_group_id = aws_security_group.scylladb_all.id cidr_ipv4 = var.your_public_network ip_protocol = "-1" # semantically equivalent to all ports } resource "aws_vpc_security_group_ingress_rule" "allow_all_internal_traffic_ipv4" { security_group_id = aws_security_group.scylladb_all.id referenced_security_group_id = aws_security_group.scylladb_all.id ip_protocol = "-1" # semantically equivalent to all ports } resource "aws_vpc_security_group_egress_rule" "allow_all_traffic_ipv4" { security_group_id = aws_security_group.scylladb_all.id cidr_ipv4 = "0.0.0.0/0" ip_protocol = "-1" # semantically equivalent to all ports } resource "aws_instance" "scylladb_seed" { ami = data.aws_ami.scylladb_ami.id instance_type = var.instance_type vpc_security_group_ids = [aws_security_group.scylladb_all.id] key_name = var.ssh_key_name user_data = <<EOF scylla_yaml: cluster_name: test-cluster experimental: true start_scylla_on_first_boot: true EOF tags = { Name = "ScyllaDB seed" } } resource "aws_instance" "scylladb_host" { ami = data.aws_ami.scylladb_ami.id instance_type = var.instance_type vpc_security_group_ids = [aws_security_group.scylladb_all.id] key_name = var.ssh_key_name user_data = <<EOF scylla_yaml: cluster_name: test-cluster experimental: true seed_provider: - class_name: org.apache.cassandra.locator.SimpleSeedProvider parameters: - seeds: ${aws_instance.scylladb_seed.private_ip} start_scylla_on_first_boot: true EOF tags = { Name = "ScyllaDB host" } count = var.number_of_regular_hosts } The main.tf file describes the infrastructure resources to be created. File Describing Outputs (outputs.tf) Plain Text output "scylladb_seed_public_ip" { value = aws_instance.scylladb_seed.public_ip description = "Public IP address of the ScyllaDB seed host." } output "scylladb_host_public_ip" { value = [aws_instance.scylladb_host.*.public_ip] description = "Public IP addresses of ScyllaDB regular hosts." } This file specifies the data to be output at the end. In our case, we want to know the IP addresses of the hosts so we can connect to them. You can also find this code on GitHub: ScyllaDB Terraform Example. How to Use This Terraform Configuration File First, you need to install Terraform and AWS CLI. Terraform installation differs across operating systems. Details can be found in the official documentation: Terraform Installation Guide. AWS CLI is a Python module that can be installed via pip in a similar way across all operating systems where Python is available. Detailed instructions are available in the official documentation: AWS CLI on PyPI. The next step is to set up security credentials for AWS CLI. Security credentials can be created using the IAM service in AWS. We assume that you already have them. To enable AWS CLI and, consequently, the AWS provider for Terraform to use your credentials, you need to configure them using the following command: Shell aws configure There are other ways to pass credentials to Terraform. More details can be found here: AWS Provider Authentication. Understanding the Variables Here’s a breakdown of all the variables: scylladb_version: The version of ScyllaDB, used in the image name to search for the AMI.your_public_network: The external IP address from which access to hosts will be allowed. It should be in CIDR format (e.g., /32 for a single address).instance_type: The type of AWS instance. You must use one of the recommended types mentioned above.number_of_regular_hosts: The number of hosts in the cluster, excluding the seed host.ssh_key_name: The name of the preloaded public SSH key that will be added to the hosts. Although variables can be overridden directly in the variables.tf file, it’s better to use a separate file for this purpose. This can be any file with a .tfvars extension, such as terraform.tfvars, located in the same directory as the Terraform configuration file. In such a file, variables are written in the format <NAME> = <VALUE>. For example: Plain Text ssh_key_name = "KEYNAME" How To Apply a Terraform Configuration To create the cluster, navigate to the directory containing the code and run the following commands: Initialize the AWS Provider: Shell terraform init Example output: Plain Text Initializing the backend... Initializing provider plugins... - Finding hashicorp/aws versions matching "~> 5.0"... - Installing hashicorp/aws v5.82.2... - Installed hashicorp/aws v5.82.2 (signed by HashiCorp) Terraform has created a lock file .terraform.lock.hcl to record the provider selections it made above. Include this file in your version control repository so that Terraform can guarantee to make the same selections by default when you run "terraform init" in the future. Terraform has been successfully initialized! Apply the Configuration: Shell terraform apply The command output will show that some parameters were taken from the configuration provided to Terraform, while others will be added automatically after applying the changes. Confirm the application by typing yes. After Terraform completes its work, it will output the public IP addresses of the hosts, which you can use to connect to ScyllaDB. Verifying Cluster Deployment To verify that the ScyllaDB cluster was successfully deployed, connect to it via SSH using the following command: Shell ssh scyllaadm@<ip-address> Once connected, you’ll immediately see the list of hosts in the cluster. Alternatively, you can run the following command: Shell nodetool status Example output: Plain Text Datacenter: eu-west =================== Status=Up/Down |/ State=Normal/Leaving/Joining/Moving -- Address Load Tokens Owns Host ID Rack UN 172.31.39.205 489.02 KB 256 ? ac814131-bac5-488b-b7f8-b7201a8dbb23 1b UN 172.31.42.145 466.77 KB 256 ? 0bd8a16f-26d3-4665-878c-74b992b91a70 1b UN 172.31.46.42 526.42 KB 256 ? 3eb8966e-b42b-48c3-9938-7f24b1a6b097 1b All hosts must have UN (Up Normal) in the first column. Adding Hosts to the Cluster ScyllaDB allows you to easily add hosts to clusters (but not remove them). Terraform, in turn, saves the state of the previous run and remembers, for example, the IP address of the seed host. Therefore, you can simply increase the number of hosts in the variable and run Terraform again. The new host will automatically join the cluster. Add the following line to your variables file: Plain Text number_of_regular_hosts = 3 In this example, it will add one more host to the cluster, but you can set the variable to any number greater than 2. Run terraform apply again. Then, log in to the seed host and verify that the list of hosts has increased. Managing Multiple Clusters You can deploy multiple clusters using a single Terraform configuration by using workspaces. Create a new workspace: Shell terraform workspace new cluster_2 cluster_2 is just an example name for a workspace. It can be anything. Deploy the New Cluster: Shell terraform apply The original cluster will remain in the workspace named default. List workspaces: Shell terraform workspace list Switch between workspaces: Shell terraform workspace select default Delete a workspace: Shell terraform workspace delete cluster_2 Destroying the Cluster To delete a ScyllaDB cluster and all associated entities, use the following command in the desired workspace: Shell terraform destroy This will clean up all the resources created by Terraform for that cluster. Conclusion With this guide, you can confidently set up, manage, and expand ScyllaDB clusters on AWS using Terraform. The step-by-step instructions provided ensure a seamless deployment experience, allowing you to focus on your application’s performance and scalability. Additionally, the flexibility of Terraform empowers you to easily adapt and scale your cluster as needed, whether it’s adding new hosts or managing multiple clusters with workspaces. For further details and advanced configurations, consult the official documentation for Terraform and ScyllaDB, which offer a wealth of resources to help you maximize the potential of your infrastructure.

Ensuring the stability and correctness of Kubernetes infrastructure and application deployments can be challenging due to the dynamic and complex nature of containerized environments. Traditional manual testing methods are time-consuming, error-prone, and insufficient for validating the integration and behavior of resources like pods, services, and deployments. There is a need for an automated, scalable, and reliable testing framework that integrates seamlessly into DevOps workflows to validate Kubernetes configurations, prevent deployment issues, and ensure system reliability across different environments. Prerequisites Ensure the following are installed on your system: Go programming language: Install Go.Kubectl: Install Kubectl.Terratest library: Install Terratest as part of your Go project dependencies.Kubernetes cluster: Have a working Kubernetes cluster (minikube, kind, or any managed Kubernetes service). Set Up Your Project 1. Create a New Go Project Open your terminal and run: Shell mkdir terratest-k8s cd terratest-k8s go mod init terratest-k8s 2. Install Terratest Dependencies Add the following to your go.mod file if not automatically installed: Shell require ( github.com/gruntwork-io/terratest/modules/k8s v0.x.x ) Install them with: Go go mod tidy 3. Write Your Test File Create a test file: Shell touch k8s_test.go Write the test. Below is an example test script: Go package test import ( "testing" "github.com/gruntwork-io/terratest/modules/k8s" "github.com/stretchr/testify/assert" ) func TestKubernetesDeployment(t *testing.T) { // Set the path to your kubeconfig file kubeconfigPath := "~/.kube/config" // Create Kubernetes options options := k8s.NewKubectlOptions("", kubeconfigPath, "default") // Apply Kubernetes manifests k8s.KubectlApply(t, options, "../manifests/deployment.yaml") defer k8s.KubectlDelete(t, options, "../manifests/deployment.yaml") // Wait for pods to become ready k8s.WaitUntilPodAvailable(t, options, "my-app-pod", 60, 5) // Get pod details and validate pods := k8s.ListPods(t, options, "app=my-app") assert.Equal(t, 1, len(pods)) } In this example: ../manifests/deployment.yaml is the path to your Kubernetes manifest.It waits for a pod with label app=my-app to be ready. 4. Run the Test Run the test using go test: Shell go test -v Review the output to ensure tests pass or debug any issues. Enhance Your Tests Validation: Add assertions to validate service responses, pod logs, or ingress behavior.Dynamic Namespaces: Use k8s.CreateNamespace and defer to manage isolated namespaces.Helm Charts: Test Helm deployments using k8s.HelmInstall. Continuous Integration (CI) Integrate Terratest into your CI/CD pipeline for automated validation: Use a pipeline tool (e.g., GitHub Actions, GitLab CI, Jenkins).Include steps to set up Kubernetes and run go test. Best Practices Use isolated test namespaces.Clean up resources with defer.Run tests against different environments (e.g., staging, production). Conclusion In conclusion, using Terratest for Kubernetes provides a robust, automated way to validate your Kubernetes infrastructure and application deployments. By following the outlined steps, you can write, execute, and maintain reliable tests for deployments, services, and pods, ensuring your Kubernetes clusters function as intended. Incorporating these tests into your CI/CD pipelines further enhances your DevOps practices, delivering faster feedback and reducing deployment risks. With Terratest, you can achieve scalable, automated testing while maintaining the integrity and stability of your Kubernetes workloads.

By Sai Sandeep Ogety CORE

AWS EC2 Autoscaling is frequently regarded as the ideal solution for managing fluctuating workloads. It offers automatic adjustments of computing resources in response to demand, theoretically removing the necessity for manual involvement. Nevertheless, depending exclusively on EC2 Autoscaling can result in inefficiencies, overspending, and performance issues. Although Autoscaling is an effective tool, it does not serve as a one-size-fits-all remedy. Here’s a comprehensive exploration of why Autoscaling isn’t a guaranteed fix and suggestions for engineers to improve its performance and cost-effectiveness. The Allure of EC2 Autoscaling Autoscaling groups (ASGs) dynamically modify the number of EC2 instances to align with your application’s workload. This feature is ideal for unpredictable traffic scenarios, like a retail site during a Black Friday rush or a media service broadcasting a live event. The advantages are evident: Dynamic scaling: Instantly adds or removes instances according to policies or demand.Cost management: Shields against over-provisioning in low-traffic times.High availability: Guarantees that applications stay responsive during peak load. Nonetheless, these benefits come with certain limitations. The Pitfalls of Blind Reliance on Autoscaling 1. Cold Start Delays Autoscaling relies on spinning up new EC2 instances when demand increases. This process involves: Booting up a virtual machine.Installing or configuring necessary software.Connecting the instance to the application ecosystem. In many cases, this can take several minutes — an eternity during traffic spikes. For example: An e-commerce platform experiencing a flash sale might see lost sales and frustrated customers while waiting for new instances to come online.A real-time analytics system could drop critical data points due to insufficient compute power during a sudden surge. Solution: Pre-warm instances during expected peaks or use predictive scaling based on historical patterns. 2. Inadequate Load Balancing Even with Autoscaling in place, improperly configured load balancers can lead to uneven traffic distribution. For instance: A health-check misconfiguration might repeatedly route traffic to instances that are already overloaded.Sticky sessions can lock users to specific instances, negating the benefits of new resources added by Autoscaling. Solution: Pair Autoscaling with robust load balancer configurations, such as application-based routing and failover mechanisms. 3. Reactive Nature of Autoscaling Autoscaling policies are inherently reactive — they respond to metrics such as CPU utilization, memory usage, or request counts. By the time the system recognizes the need for additional instances, the spike has already impacted performance. Example: A fintech app processing high-frequency transactions saw delays when new instances took 5 minutes to provision. This lag led to compliance violations during market surges. Solution: Implement predictive scaling using AWS Auto Scaling Plans or leverage AWS Lambda for instantaneous scaling needs where possible. 4. Costs Can Spiral Out of Control Autoscaling can inadvertently cause significant cost overruns: Aggressive scaling policies may provision more resources than necessary, especially during transient spikes.Overlooked instance termination policies might leave idle resources running longer than intended. Example: A SaaS platform experienced a 300% increase in cloud costs due to Autoscaling misconfigurations during a product launch. Instances remained active long after the peak traffic subsided. Solution: Use AWS Cost Explorer to monitor spending and configure instance termination policies carefully. Consider Reserved or Spot Instances for predictable workloads. Enhancing Autoscaling for Real-World Efficiency To overcome these challenges, Autoscaling must be part of a broader strategy: 1. Leverage Spot and Reserved Instances Use a mix of Spot, Reserved, and On-Demand Instances. For example, Reserved Instances can handle baseline traffic, while Spot Instances handle bursts, reducing costs. 2. Combine With Serverless Architectures Serverless services like AWS Lambda can absorb sudden, unpredictable traffic bursts without the delay of provisioning EC2 instances. For instance, a news website might use Lambda to serve spikes in article views after breaking news. 3. Implement Predictive Scaling AWS’s predictive scaling uses machine learning to forecast traffic patterns. A travel booking site, for example, could pre-scale instances before the surge in bookings during holiday seasons. 4. Optimize Application Performance Sometimes the root cause of scaling inefficiencies lies in the application itself: Inefficient code.Database bottlenecks.Overuse of I/O operations.Invest in application profiling tools like Amazon CloudWatch and AWS X-Ray to identify and resolve these issues. The Verdict EC2 Autoscaling is an essential component of modern cloud infrastructure, but it’s not a perfect solution. Cold start delays, reactive scaling, and cost overruns underscore the need for a more holistic approach to performance tuning. By combining Autoscaling with predictive strategies, serverless architectures, and rigorous application optimization, organizations can achieve the scalability and cost-efficiency they seek. Autoscaling is an impressive tool, but like any tool, it’s most effective when wielded thoughtfully. For engineers, the challenge is not whether to use Autoscaling but how to use it in harmony with the rest of the AWS ecosystem.

Anyone working in DevOps today would likely agree that codifying resources makes it easier to observe, govern, and automate. However, most engineers would also acknowledge that this transformation brings with it a new set of challenges. Perhaps the biggest challenge of IaC operations is drifts — a scenario where runtime environments deviate from their IaC-defined states, creating a festering issue that could have serious long-term implications. These discrepancies undermine the consistency of cloud environments, leading to potential issues with infrastructure reliability and maintainability and even significant security and compliance risks. In an effort to minimize these risks, those responsible for managing these environments are classifying drift as a high-priority task (and a major time sink) for infrastructure operations teams. This has driven the growing adoption of drift detection tools that flag discrepancies between the desired configuration and the actual state of the infrastructure. While effective at detecting drift, these solutions are limited to issuing alerts and highlighting code diffs, without offering deeper insights into the root cause. Why Drift Detection Falls Short The current state of drift detection stems from the fact that drifts occur outside the established CI/CD pipeline and are often traced back to manual adjustments, API-triggered updates, or emergency fixes. As a result, these changes don’t usually leave an audit trail in the IaC layer, creating a blind spot that limits the tools to just flagging code discrepancies. This leaves platform engineering teams to speculate about the origins of drift and how it can best be addressed. This lack of clarity makes resolving drift a risky task. After all, automatically reverting changes without understanding their purpose — a common default approach — could be opening a can of worms and can trigger a cascade of issues. One risk is that this could undo legitimate adjustments or optimizations, potentially reintroducing problems that were already addressed or disrupting the operations of a valuable third-party tool. Take, for example, a manual fix applied outside the usual IaC process to address a sudden production issue. Before reverting such changes, it’s essential to codify them to preserve their intent and impact or risk prescribing a cure that could turn out to be worse than the illness. Detection Meets Context Seeing organizations grapple with these dilemmas has inspired the concept of ‘Drift Cause.’ This concept uses AI-assisted logic to sift through large event logs and provide additional context for each drift, tracing changes back to their origin — revealing not just ‘what’ but also ‘who,’ ‘when,’ and ‘why.’ This ability to process non-uniform logs in bulk and gather drift-related data flips the script on the reconciliation process. To illustrate, let me take you back to the scenario I mentioned earlier and paint a picture of receiving a drift alert from your detection solution — this time with added context. Now, with the information provided by Drift Cause, you can not only be aware of the drift but also zoom in to discover that the change was made by John at 2 a.m., right around the time the application was handling a traffic spike. Without this information, you might assume the drift is problematic and revert the change, potentially disrupting critical operations and causing downstream failures. With the added context, however, you get to connect the dots, reach out to John, confirm that the fix addressed an immediate issue, and decide that it shouldn’t be blindly reconciled. Moreover, using this context, you can also start thinking ahead and introduce adjustments to the configuration to add scalability and prevent the issue from recurring. This is a simple example, of course, but I hope it does well to show the benefit of having additional root cause context — an element long missing from drift detection despite being standard in other areas of debugging and troubleshooting. The goal, of course, is to help teams understand not just what changed but why it changed, empowering them to take the best course of action with confidence. Beyond IaC Management But having additional context for drift, as important as it may be, is only one piece of a much bigger puzzle. Managing large cloud fleets with codified resources introduces more than just drift challenges, especially at scale. Current-gen IaC management tools are effective at addressing resource management, but the demand for greater visibility and control in enterprise-scale environments is introducing new requirements and driving their inevitable evolution. One direction I see this evolution moving toward is Cloud Asset Management (CAM), which tracks and manages all resources in a cloud environment — whether provisioned via IaC, APIs, or manual operations — providing a unified view of assets and helping organizations understand configurations, dependencies, and risks, all of which are essential for compliance, cost optimization, and operational efficiency. While IaC management focuses on the operational aspects, Cloud Asset Management emphasizes visibility and understanding of cloud posture. Acting as an additional observability layer, it bridges the gap between codified workflows and ad-hoc changes, providing a comprehensive view of the infrastructure. 1+1 Will Equal Three The combination of IaC management and CAM empowers teams to manage complexity with clarity and control. As the end of the year approaches, it's 'prediction season' — so here’s mine. Having spent the better part of the last decade building and refining one of the more popular (if I may say so myself) IaC management platforms, I see this as the natural progression of our industry: combining IaC management, automation, and governance with enhanced visibility into non-codified assets. This synergy, I believe, will form the foundation for a better kind of cloud governance framework — one that is more precise, adaptable, and future-proof. By now, it’s almost a given that IaC is the bedrock of cloud infrastructure management. Yet, we must also acknowledge that not all assets will ever be codified. In such cases, an end-to-end infrastructure management solution can’t be limited to just the IaC layer. The next frontier, then, is helping teams expand visibility into non-codified assets, ensuring that as infrastructure evolves, it continues to perform seamlessly — one reconciled drift at a time and beyond.

There are two misconceptions when it comes to testing. One of them is that developers alone can test products to reduce costs. The second is that nothing much has changed since automated testing came into play. The truth is? The testing industry is evolving by leaps and bounds, and if you don’t invest in testing, the cost of glitches can be extremely high. You may have heard about Bangladesh Bank, which was hacked, and $81 million was stolen. However, they could have easily prevented this if not for a system glitch that interrupted the printing process and made it impossible to detect suspicious transactions in time. This is just one example of how cutting testing costs can have devastating consequences, but the idea is clear: testing isn’t the area that should be curtailed. Vice versa. It’s important to stay up to date with what’s happening in the world of testing and invest in the latest trends. In this article, we’ve compiled some of the most prominent automation testing trends for 2024 that have taken off this year and are set to continue. Be sure to explore the benefits of each and see how they can fit your testing strategies. Key Takeaways Testing evolves by leaps and bounds, demanding QA teams to stay ahead of the curve. Keeping pace with trends in automation testing is crucial for ensuring that testing strategies are effective, scalable, and aligned with the latest technological advancements.One of the latest trends in automation testing is quantum computing. Although still in its early stages, quantum computing promises faster, more precise testing, tackling complex systems that traditional methods struggle with.Shift-left testing doesn’t lose its relevance. It continues to evolve, pushing testing even earlier in the development cycle, which reduces costs and accelerates bug detection before major issues arise.Along with shift-left testing, scriptless test automation and codeless automation are becoming more popular, allowing a wide range of professionals, regardless of their technical expertise, to create and run tests without writing code. The future of testing automation looks promising. As technologies continue to advance, testing practices are becoming smarter and more efficient, ensuring faster releases and higher-quality software products. 18 Automation Testing Trends for 2024 Some trends are just the flash in the pan. In theory, they are full of promise, but come real-case tasks, they often reveal their limitations and impracticality. We’ve hand-picked test automation trends for 2024 that seem both realistic to implement and have the potential to truly make a difference in how we assure quality. 1. QAOps Is Gaining Popularity QAOps is a booming trend in 2024. As the name suggests, this methodology combines a quality assurance (QA) approach with IT operations to speed up testing. In other words, it integrates quality assurance and testing into the DevOps process, introducing thorough testing at every stage of the software development cycle, not just at the end. To elaborate, the QAOps framework allows developers and testers to work together. Through this close-knit collaboration, companies can pinpoint the different scenarios that real users might encounter when they start interacting with the app, thereby improving the testing process. Some companies have given this approach their own twist. They organize so-called “testing parties,” inviting all interested team members to take part in testing. By doing so, they not only ensure there’s no bias in testing, but they also get the opportunity to test their products from different perspectives, allowing them to ultimately achieve better outcomes. It’s like building a house where developers build the foundation, walls, and roof, and testers ensure from the beginning that all of these components are made strong. Later, when the house is ready for exploitation, no major rework will have to be done. Benefits Improved collaboration between testers, developers, and IT operations teams.Boosted the productivity of the team through ongoing knowledge sharing.Faster release of new products and features due to the quick detection of bugs.Enhanced customer experience achieved by delivering software products of a high-quality standard. 2. Extensive Adoption of Robotic Process Automation Another automated testing trend that is growing in popularity is the use of Robotic Process Automation (RPA). RPA, also known as software robotics, has the ability to mirror testers’ interactions with software applications. By recording actions performed by testers and learning testing sequences, it can imitate the same process, saving you lots of time performing repetitive tasks. RPA has become widely popular due to the ease and speed of its implementation. Compared to traditional automation, which requires specialized hardware and software to automate repetitive tasks, RPA uses bots. Often run by humans, these bots can be easily programmed using off-the-shelf software and trained in a matter of hours or a couple of days, making RPA a more cost-effective testing solution for most companies. RPA testing techniques are being used across various industries and will become even more widespread in the near future. According to Statista, the RPA market will grow to $13.39 billion by 2030, which is a huge jump from $3.17 billion in 2022. Moreover, RPA will become much more intelligent thanks to the advances in artificial intelligence and machine learning soon. This means that RPA will not only copy human actions but also understand information, learn from experience, and proactively address issues. Ultimately, this will allow it to execute more complex decision-making processes without much guidance on the tester’s part. Benefits Reduced labor costs due to the automation of repetitive test scenarios.Less of a risk for human error, as RPA accurately replicates testers’ interactions. Significant time and resource savings due to RPA bots’ ability to execute tests across multiple systems and applications. Scalability that allows organizations to perform small, large, or even enterprise-level tests on demand. 3. Active Use of AI and ML Testing AI and ML tools are making a splash in the testing industry. With their ability to automate virtually every aspect of testing, from test case creation and test execution to test maintenance, they become indispensable for QA teams. The practical applications of AI and ML in automated testing are numerous. From identifying features for testing, creating test cases without manual test scripts, and running thousands of tests virtually in minutes, it can do all that and much more without human support. As we progress in the future, the capabilities of AI and ML are expected to only evolve. In the near term, it will be possible to digitize testing processes by creating AI-powered avatars of famous testers, such as Bret Pettichord, Cem Karner, Tariq, you name it. By imparting their knowledge and expertise to bots, organizations will be able to create strong virtual teams of testers that can execute all types of testing and adequately measure the quality of a project based on input derivable. Other examples of using AI and ML in testing include: Natural language processing: NLP algorithms can extract and analyze requirements from natural language documents, helping with test case creation and ensuring that tests align with the project’s goals. Visual testing with computer vision: AI-driven computer vision systems can automatically compare UI elements and screens to identify visual defects or inconsistencies in applications. Automated bug triaging: ML models can help prioritize and categorize incoming bug reports.Behavior-driven testing with AI: AI can understand user behavior patterns and generate test scenarios that mimic real-world user interactions, enabling QA teams to cover more test cases. Security testing: AI can simulate cyberattacks and identify vulnerabilities in software. Aside from that, AI excels at predicting outcomes. By analyzing historical data and patterns, it can identify with a high probability what bugs and issues are likely to occur and help prevent them before they become major issues. “Remember, AI tools are just that — tools. They are there to augment our abilities, not to replace testers. So, whether you’re looking to leverage AI automation to improve workflows or optimize testing approaches, the resolution of these issues requires human insight and ingenuity.” - Taras Oleksyn, QA Lead Benefits Faster release cycles;Quick test case generation;Automatic test maintenance;Predicting the likelihood of bugs and testing outcomes based on historical data;An extensive test coverage across various devices ensures that the software is thoroughly tested and no bugs slip through the cracks. 4. Explainable AI is Gaining Traction While AI is confidently working its way into testing, helping QA teams improve the accuracy of the results and mitigate errors, the main thing that makes this testing useful is transparency. QA engineers need to be able to understand why this or that decision was made and act on them accordingly. That’s where explainable AI plays a pivotal role. In layman’s terms, explainable AI (often referred to as “explainability”) is the ability of testers to determine what exactly accuracy means in each particular case and predict future decisions based on derived results. With clear definitions of AI results, they can prevent bugs, defects, and biases, ensure their products are regulatory compliant, and build trust with key stakeholders. In the coming years, explainable AI will become an integral part of testing. We will see the emergence of many new AI frameworks and tools like SHAP, TensorFlow, Lime, and similar, which will speed up the adoption of explainable AI in software testing. Benefits Early detection of bugs, reducing the cost and time to fix them.Higher quality software as testing is performed from the start of development.Shorter development cycles due to faster feedback on code changes.Reduced risk of late-stage failures that can delay product releases. 5. Focus on Ethical Testing Will Increase In 2024, the focus on ethical testing will increase even more as organizations recognize the importance of responsible software development. Ethical testing embraces more than just the technicalities of software functionality. First and foremost, it’s about ensuring that technology is safe and fair and understanding how it has arrived at this or that outcome. The main concern with AI is that it’s been operating as a “black box” for a long time. It generates results, but why and how it has made these decisions is not always clear, often leading to biases and discriminatory outcomes. Ethical testing addresses these concerns. With ethical testing, QA teams focus on mitigating biases early in development, ensuring that algorithms are fair and transparent and do not perpetuate discrimination related to race, gender, or socioeconomic factors. Seeing how much attention is paid to AI ethics today, ethical testing is moving from a nice-to-have to a must-have practice and becoming one of the significant trends in automation testing for 2024 and beyond. Benefits Provides clear insights into how AI systems make decisions.Helps identify and address potential biases early in development.Ensures alignment with global regulations and standards.Builds confidence among users and stakeholders. 6. A Shift to Self-Healing Tools One of the biggest hurdles in automated testing is flakiness. When tests show different results after each run, knowing which one of them is accurate becomes a challenge. Self-healing tools aim to address this challenge. By automatically adjusting test scripts when changes occur in the application under test, they can ensure that tests remain stable and QA teams have fewer supporting tests to do. The best thing is that self-healing tools constantly learn. With each run, they process more data and patterns and identify recurring issues, becoming more efficient at creating tests. This, along with their ability to process volumes of data going through developer’s pipelines, makes them essential for modern QA processes requiring both accuracy and reduced downtime. Benefits Proactive issue detection, minimizing downtime.Seamless integration with CI/CD pipelines, ensuring up-to-date and reliable test scripts. AI-driven prioritization of test cases.Continuous adaptation to application changes, requiring minimal manual intervention. 7. Blockchain Testing Is Becoming a Hot Trend While you most definitely have heard of blockchain, you might not have stumbled upon the possibilities blockchain testing brings to the table. Blockchain testing can be used for a variety of applications. From securing transactions to ensuring the integrity of supply chains and even verifying the authenticity of digital assets, it renders itself useful in a wide range of industries and scenarios that need accurate data validation. If you’re looking to ensure peak Decentralized Applications (DAPPs) performance, definitely consider blockchain testing. Blockchain provides seamless scalability to DAPPs, ensuring they can efficiently handle increased volumes of data and transactions. Moreover, conducting comprehensive tests helps identify potential vulnerabilities, inefficiencies, and bottlenecks that may hinder the smooth operation of DAPPs. However, it’s worth noting that blockchain testing is conceptually different from other types. It’s made of components such as smart contracts, nodes, blocks, consensus mechanisms, transactions, and wallets, which need to be thoroughly tested and require a relevant stack. Benefits No risk of unauthorized access, fraud, and data breaches thanks to robust security protocols.Quick removal of roadblocks that could become a costly problem in the production environment. Compliance with industry regulations and data protection laws, which can be particularly important for industries like finance and healthcare. Confidence that a blockchain is thoroughly tested and safe to use. 8. More Organizations Will Leverage Shift-Left Testing Many organizations are embracing Shift-Left Testing. This practice isn’t new, but it has evolved over time and shifted even further towards the left in the pipeline in 2024. Instead of waiting for code to be developed, teams now write unit tests before the coding phase begins. Involving testers early in the development cycle offers some undeniable advantages. And one of the most important of them is cost reduction. By checking the validity of the code early on, teams can identify and fix bugs while the cost to fix them is still rather low, rather than letting them escalate to later stages, where it can reach $7,600 and more. The Shift-Left approach also encourages the use of intelligent analytics. The Shift-Left approach also encourages the use of intelligent analytics. Testers can gauge customer satisfaction by monitoring their interactions with the software. If they find that the software needs some changes, they can introduce them during the initial stages of software development when they won’t cost much. It’s worth noting that despite the benefits shift-left testing offers, it’s not always appropriate to involve testers early in the development process. The decision to involve QA testing teams should be made based on the state of a project. “If a project is in its initial stages and the functionality is constantly changing, it wouldn’t make sense to start automation. The project has to reach a certain level of stability before it’s appropriate to automate.” - Taras Oleksyn, QA Lead Benefits Reduced development costs through early detection and correction of bugs and errors.The possibility to automate test cases earlier and streamline the entire testing process.Faster time to market thanks to the optimized QA process. 9. The Demand for Cloud-Based Cross Browser Testing Is Increasing Among the growing trends in test automation adopted by organizations this year, cloud-based cross-browser testing stands out. As the variety of devices increases yearly, it’s become essential for companies to thoroughly test their solutions across all of them. However, in practice, achieving such extensive coverage is often out of reach for many small companies because it is expensive to build such an extensive testing infrastructure. As a result, an increasing number of companies are turning to third-party providers that offer access to cloud technologies and thousands of virtual environments for testing. Sure enough, with the emergence of cloud testing platforms, the market has also witnessed a rise in cloud-based testing tools. These tools provide support for all popular browsers and devices, enabling QA teams to create and run cross-compatible tests. The global cloud application market’s value is expected to rise from $171 billion in 2020 to $365 billion by 2025. Benefits No need for costly in-house testing infrastructure, which means companies pay only for the resources they use.An extensive testing coverage, including a wide range of devices, browsers, operating systems, and screen sizes.The ability to scale testing resources up or down based on the project’s needs.Support for parallel testing across most cloud-based platforms, which significantly reduces testing timelines and speeds time to market. 10. Exploratory Testing Will Become Inevitable Exploratory testing has emerged as a practice that veers away from rigid test cases and scripts. Instead, it gives testers the freedom to explore and test software intuitively. This element of randomness not only allows QA teams to uncover unique use cases that haven’t been described by scripted testing but also find issues in areas where they wouldn’t typically look. Benefits Eliminating the need to document test cases or features speeds up testing. The ability to catch issues and bugs that other testing methods and techniques might miss.Quick start due to the lack of extensive test case preparation. This is especially important in situations where there’s limited documentation or time for test case creation. 11. Microservices Testing Rapidly Evolves The popularity of microservices architecture has given rise to microservices testing. This testing approach is aimed at testing the software as a suite of small, individual functional pieces rather than the entire architecture and closely monitoring the ongoing performance. Seeing how microservice-based applications rapidly appear on the market, microservices testing will continue to evolve, and the skill of testing microservices will be in high demand. Benefits The ability to test individual components and changes in one microservice without affecting others.Faster development cycles due to the option to release and iterate right on microservices.Faster project delivery thanks to the ability to work on multiple microservices at the same time. Optimized usage of resources, which leads to cost savings. 12. In-Sprint Automation Is Expected to Grow According to Marketsplash, 71% of organizations are adopting agile methodologies, and more companies are considering embracing agile in the coming years. This trend has driven the growth of in-spirit automation. In-sprint automation refers to the integration of test automation efforts within each sprint or iteration of the agile development process. By following this approach, companies can significantly speed up their release cycles, consolidating all the fundamental functions of testing in short increments. Benefits In-sprint automation, which allows for faster and more frequent releases. Automated tests run continuously during development, enabling early detection of defects and issues. Real-time feedback on the quality of the software, which allows for more accurate project planning, better resource allocation, and improved forecasting of project timelines.Automated tests created during one sprint can be reused in subsequent sprints for regression testing. Quick validation of new requirements and the ability to tailor the testing approach accordingly. 13. Integration of Crowdsourced Testing Crowdsourced testing is a progressive approach to software QA that engages a diverse community of testers, often from around the world, to test products under real-world conditions on real devices. The great thing about crowdsourcing is that it allows companies to avoid resource constraints. They don’t have to worry about whether the tester has the right test automation tools or skills. Instead, tasks are distributed according to the resources that the tester already has, which greatly speeds up the time to market. Crowdsourced testing is often used to accelerate automation, in particular when the company is on the brink of a product release and/or is looking to extend its reach to global markets. In the future, it will be used more extensively as companies realize the positive impact of involving end users in the testing process. Benefits The ability to tap a diverse testing experience.An extended test coverage comprising an array of devices, operating systems, browsers, and resolutions.An easy way to scale up or down testing capacity depending on the project’s needs.Involving end users in the process contributes to the delivery of a well-received product. 14. Scriptless Test Automation This testing practice implies using automation testing tools to evaluate software quality without traditional scripts or code. The concept is simple. The tools record the actions that testers take while navigating through the software and then, based on the results, generate the most likely use cases for different scenarios. Scriptless test automation platforms are designed to perform all types of testing, including UI/UX testing, functional testing, etc., making them suitable for many different projects. However, similar to other tools, scriptless test automation platforms have limitations when it comes to customization. While this limitation may not be an issue for most projects with straightforward requirements, it can pose constraints for highly complex applications. In such cases, go for traditional script-based automation. Benefits Speeding up the product delivery process.Higher ROI due to reduced automation costs.Flexibility in reusing automation scripts in various scenarios. 15. Codeless Automation Goes Mainstream One of the other test automation trends that goes mainstream in 2024 is codeless automation. Just as the name suggests, this type of testing involves no coding, making it accessible to a wide range of professionals regardless of their technical background. QA engineers, business analysts, or even non-technical team members can create and run automated tests without needing to write code. Aside from that, codeless automation reduces the time needed for the test creation, making the process of writing test scripts less time-consuming and laborious, ultimately speeding the development cycle. Although not without limitations, especially when it comes to handling complex or highly customized applications requiring specific testing scenarios, codeless automation is proving to be a game-changer for projects with simple workflows. No wonder low-code and no-code platforms are gaining traction so rapidly. In fact, its market is expected to bloom by 2027 with an estimated value of around $65 billion globally, showing the growing demand for simpler automation solutions. Benefits Expands test automation capabilities to non-technical team members.Reduces the time and effort required to create and maintain test scripts.Speeds up the development and release cycles, supporting faster delivery of high-quality products.Lowers dependency on highly specialized automation engineers, allowing teams to be more self-sufficient. 16. Continuous Testing Will Simplify Build Releases Continuous testing (CT) helps businesses to evaluate risks associated with software launch, ensuring informed decisions about whether to proceed or make adjustments. CT is performed after each product change and can be integrated into the CI/CD pipeline. As businesses increasingly recognize the benefits of CT, its demand and adoption are expected to continue growing in the software testing industry. According to Report and Data, 21% of QA testers have already incorporated CT into their processes to accelerate code releases, while the rest are keen on doing so in the near future. Benefits Early detection of defects and issues in the software development life cycle, which helps reduce the cost and effort required to fix them.Adaptability to various development methodologies, including Agile, DevOps, and Waterfall. Fast delivery of high-quality software.Ability to access testing reports at any point. 17. Mobile Automation Comes at the Frontier The recent surge in mobile production has brought about the importance of mobile test automation. In 2024 and the upcoming years, as the number of devices continues to grow, mobile app testing will become even more widespread. Companies will exponentially invest in robust mobile automation tools to stay competitive and will be looking for testers with relevant experience. Benefits Reliable app functioning in all parts of the world.Faster deployment times due to streamlined testing activities.Meticulous functioning of the app, including its UI and UX.100% test coverage. 18. Quantum Computing Emerges as a Game-Changer One of the latest trends in automation testing is the integration of quantum computing. While still in its early stages, quantum computing can enable computational capabilities far beyond those of traditional systems, allowing it to process massive datasets in parallel. It’s already been implemented in banking to help with fraud and risk management, but the potential applications extend much further. In testing, the emergence of quantum computing opens up a new niche that demands new specialized skills and expertise. To keep up with the pace, QA engineers will need to develop a deep understanding of quantum algorithms, qubit operations, and quantum data processing. This automation testing trend is expected to lead to the creation of entirely new testing frameworks designed specifically for quantum-powered systems. Benefits Ability to test complex systems more quickly and efficiently than traditional methods.Ability to perform highly complex calculations with greater precision.Simulation of complex test scenarios that traditional systems struggle with, such as multi-variable simulations or optimization problems.Reduced time needed for testing. The Driving Force Behind Test Automation Trends As you can see, there are a lot of things going on in the market, and while not all trends are long-lasting, some may be too costly to miss. Therefore, it’s important to stay abreast of the latest trends in order not to miss out on opportunities that can amp up your testing practices. Now, the question is: what is driving the change in test automation? And is there a way to predict the trends that are looming on the horizon? Well, you can definitely anticipate certain trends if you keep up with the shifts in the technology world. Here are some of the things that have driven transformation in recent years and are going to have a big impact on the testing landscape in 2024 and beyond. Technological advancements: The ongoing evolution of technology, such as AI and machine learning, is a major driver. These technologies enable smarter and more efficient testing practices.Changing user expectations: As users demand more seamless and user-friendly software experiences, testing practices always evolve to ensure software meets these expectations. Agile and DevOps adoption: The widespread adoption of Agile and DevOps methodologies has not gone unnoticed in the testing industries. With the rising number of teams embracing agile and combining dev and operational processes, CT and integration have become the new norm. Security concerns: As the amount of data exchanged over the Internet grows rapidly, security has become paramount. Testing practices address these concerns by focusing more on identifying vulnerabilities, ensuring robust security measures, and safeguarding sensitive data.Market dynamics: Of course, competition and market demands play a crucial role in driving trends as well. Products need to be high quality and delivered quickly, which pushes organizations to adopt and adapt to the latest testing practices. Evolving software architecture: Changes in software architecture, such as microservices and cloud-based solutions, require new testing approaches to ensure compatibility and reliability. Remote work: The rise of remote work has a direct impact on collaboration, testing environments, and tools used in testing. These driving forces, along with emerging technologies and methodologies, shape the future of test automation. By keeping a close eye on these factors, organizations can anticipate upcoming trends and make informed decisions to strengthen their testing practices. Conclusion As we’ve covered so many test automation trends, you may be wondering if all of them are going to still be relevant beyond 2024. The answer is — not necessarily, although they are all very popular these days. Looking ahead, we can make an assumption that scriptless and codeless automation testing won’t last too long due to their restrictions. At the same time, trends like AL, ML, RPA, and blockchain will gain more strength as websites and applications become more sophisticated. What’s also definite is that software test automation will not go anywhere anytime soon. It will become a big thing, and the sooner you adapt and start using the technology — the better.

Microservices have become a popular architectural style for building scalable and modular applications. However, setting up a microservice from scratch can still feel complicated, especially when juggling frameworks, templates, and version support. ServiceBricks aims to simplify this by allowing you to quickly generate fully functional, open-source microservices based on a simple prompt using artificial intelligence and source code generation. In this tutorial, we’ll walk through the entire process — from signing up to running your newly created microservice locally. Getting Started Prerequisites A web browserVisual Studio 2022 Step 1: Register for an Account Begin by registering for an account on the ServiceBricks.com website. Once you’ve signed up and logged in, you’ll have access to the platform’s microservice generation features and receive 10 free credits to evaluate the product. Step 2: Create a New Microservice After logging in: Locate and click the button to "Create Microservice."Enter a prompt or description of what you want your microservice to accomplish. For example, this could be “Create a microservice to manage a grocery list.”Click the "Generate" button. Step 3: Download the Generated Source Code The generation process will create a tailored microservice solution for you. On the summary screen: Review the key details provided to ensure the microservice meets your requirements.Click the "Download Source Code" button. This will download a zip file that contains a complete solution containing multiple application projects including unit and integration tests for different .NET Core versions. Running Your Microservice Locally Once you have the source code, open the solution in Visual Studio 2022. Step 1: Set the Startup Project In the Solution Explorer, find the project for your chosen .NET Core version (e.g., WebAppNet8).Right-click the project and select "Set as Startup Project." Step 2: Run the Application Once the startup project is set, click the "Start" button (or press F5) in Visual Studio.Visual Studio will build and run the application. By default, it should start running at https://localhost:7000. Step 3: Explore the Home Page Instructions With the microservice running locally, the home page provides instructions on how to interact with your new microservice, as well as details on configuring storage providers or other backend services. By default, the web application will use the InMemory database provider. To use any of the relational database providers, you will need to create migrations so that the database can be created. Step 4: Create Migrations for Relational Databases Run the Batch File Within the source code folder of your project, run the CreateMigrations.cmd file. This will: Trigger a solution build.Generate database migrations for the following providers: PostgresSQLiteSQL Server Now that your microservice is up and running, you can begin experimenting by sending requests to the endpoints defined in your service. You can also modify the underlying code to add new features, integrate custom logic, or switch out the default storage provider. Conclusion By following these steps, you’ve seen how to take a prompt and transform it into a running microservice, ready for integration into a larger application ecosystem. You’ve learned how to register, generate code, download the source, run the application, and manage database migrations. With this solid starting point in place, you can now focus on refining and extending your service’s functionality, adjusting storage providers, and tailoring it to fit the unique requirements of your project.

As businesses grow and cloud systems get more complex, traditional DevOps methods struggle to keep up with fast changes. That’s where Generative AI comes in. This new technology is changing how applications are made and used. It is also evolving DevOps practices by automating repetitive tasks, improving processes, enhancing security, and providing better monitoring insights. AI has become a crucial partner for DevOps teams that aim for agility and strength in a rapidly changing cloud world. In this article, we will look closely at how Generative AI is transforming DevOps. We will talk about the challenges and opportunities it brings. We will also see how Microtica is leveraging AI to help DevOps teams deliver cloud solutions that are smarter, faster, and more efficient. Understanding the Impact of AI on DevOps DevOps focuses on automation, integration, and continuous delivery. This makes it a great fit for AI to enhance its abilities. In traditional DevOps, teams automate repetitive tasks, monitor systems in real time, and ensure that security practices are intact. However, as applications grow and cloud systems become more distributed, the amount of data and the difficulty of these tasks increase significantly. This is where AI is very important. By using machine learning and big data, AI can analyze, predict, and optimize processes more efficiently than human teams. AI can find patterns and problems quickly, offering improvements and making tasks easier. This speeds up the DevOps lifecycle a lot. In simple terms, AI helps teams work faster and smarter, enabling them to focus on strategic decisions in the development process, while AI takes care of the hard work. Exploring Generative AI's Role in Evolving DevOps Practices Automation: The Next Level of Efficiency Automation has always been essential in DevOps. Now, Generative AI makes it even better. Regular automation scripts use set rules and steps. They help with tasks like code deployment and monitoring. However, these systems still need manual updates to get better over time. Artificial intelligence changes this by allowing self-learning automation. This means the system can execute tasks and learn from past performances. This way, future workflows can be made more efficient. For example, AI can create scripts for infrastructure management using past data. This reduces the need for manual work. If a certain application often has performance problems with specific resources, AI can automatically adjust those resources in future setups. This smart automation reduces human misconfigurations in software delivery and improves scalability, making it easier to manage larger infrastructures without needing more team members. Intelligent CI/CD Pipelines: Optimizing Continuous Delivery One of the biggest impacts of AI on DevOps is in Continuous Integration and Continuous Delivery (CI/CD) pipelines. These pipelines help automate how code changes are managed and deployed to production environments. Automation in this area makes operations more efficient. However, as codebases grow and get more complex, these pipelines often need manual tuning and adjustments to run smoothly. AI impacts this by making pipelines smarter. It can analyze historical data, like build times, test results, and deployment patterns. By doing this, it can adjust how pipelines are set up to minimize bottlenecks and use resources better. For example, AI can decide which tests to run first. It chooses tests that are more likely to find bugs from code changes. This helps to speed up the process of testing and deploying code. AI can detect when a pipeline is underperforming, suggest changes to make it better, or even make those changes itself. This may include rerouting tasks, boosting resources when traffic is high, or scaling down resources when you don't need them. At Microtica, we are focused on bringing this AI-driven optimization into the CI/CD process. We envision a future where pipelines are automated and intelligent, learning from previous iterations to become more efficient over time. Our goal is to help DevOps teams deploy their code more quickly and safely. As their code and systems grow, they will not need to make as many manual changes. Predictive Security: Proactive Defense with AI Security has always been very important for cloud-native apps and DevOps teams. With Generative AI, we can now move from reactive to proactive when it comes to system vulnerabilities. Instead of just waiting for security issues to appear, AI helps DevOps teams spot and prevent potential risks ahead of time. AI-powered security tools can perform data analysis on a company’s cloud system. They can spot patterns that might show the start of a security problem. For instance, AI can find strange login activities, sudden increases in traffic that might mean a DDoS attack, or changes to system settings that are not allowed, which could indicate a vulnerability. At Microtica, we believe that security is a key part of our cloud delivery platform. We are working on incorporating AI-driven security features, to help teams detect threats in real-time and also predict potential issues. This way, we can lower the chance of downtime or losing data. We want to make sure that security does not slow down the DevOps process. Monitoring and Observability: Gaining Actionable Insights In DevOps, observability is crucial to keep systems healthy. Traditional tools, such as Prometheus and Grafana, do a great job of collecting metrics and logs. However, understanding these data points to get useful insights takes time and expertise. Generative AI changes this by automating the process of understanding the data. This helps teams get insights more quickly and accurately. With AI-powered observability, DevOps teams can spot issues and performance problems in real time. They also get tips on how to solve these problems. For example, if an app’s response time increases suddenly, AI can find the main cause. This might be a misconfiguration, a lack of resources, or a problem with another service. Then, it can suggest a way to fix it or even implement the fix. At Microtica, we are committed to integrating these AI-driven monitoring capabilities into our platform. With these tools, we provide real-time, actionable insights that help DevOps teams. This way, they can fix problems quicker and prevent them from happening again. Cost Optimization: Balancing Performance and Expense Cloud environments are very flexible, but they can get expensive if you do not manage resources well. Generative AI can help reduce costs by changing how resources are used based on real-time data. AI algorithms can predict when resources are underutilized and can scale them down. They can also scale up resources when a high demand is expected. This ability to right-size cloud infrastructure not only ensures optimal performance in deployment processes but also helps teams avoid over-provisioning, reducing unnecessary cloud expenses. By using AI capabilities, you can also understand which services use the most resources and explore ideas on how to optimize them. At Microtica, we see cost optimization as a key area where AI can deliver immediate value. Our platform is designed to help teams strike the perfect balance between performance and cost, ensuring that resources are used efficiently while minimizing expenses. What Are the Challenges and Opportunities of AI in DevOps? AI is revolutionizing DevOps, but it brings some challenges, too. There may be problems with data quality, security vulnerabilities, and over-reliance on automation. Still, the opportunities, like better security, automation, and cost optimization, outweigh the risks. This makes AI a key player for making DevOps faster and more effective. Let's take a look at the challenges that teams must navigate. One big issue is data quality. AI depends on the quality and accuracy of its input data to work well. If the data is not reliable, AI can make wrong predictions. This can result in poor results or even harmful effects. Another challenge is finding the right balance between automation and human control. Automation can be helpful and save time. However, depending too much on AI for decision-making can lead to consequences, especially if teams do not keep an eye on things. There is always a chance that AI will make poor choices if it is not correctly configured or monitored. Security is like a double-edged sword. AI can improve security, but it can also create new vulnerabilities. AI systems can be targets for hackers, who may take advantage of weaknesses in algorithms to gain unauthorized access or disrupt services. Despite these challenges, there are many great opportunities. AI improves the efficiency of DevOps. It also brings new possibilities for innovation. With the help of AI, teams can use smart predictions, automate tasks, and manage resources better. This way, they can focus on what really matters—delivering value to users. Conclusion and the Future of AI in DevOps The future of DevOps depends on how well we use Generative AI. As cloud environments become more complex, DevOps teams face greater demands. AI will play an even more critical role in helping teams deliver results quickly while keeping quality and security intact. Though there are some challenges to deal with, the advantages are much greater than the risks. AI will keep unlocking new methods for innovation and efficiency.

By Marija Naumovska CORE

Getting things right requires iteration. And we all know that requires running screens — they engage users far more than documents or wireframes. Framework complexity leads to this unpleasant situation: months of effort to get running screens, only then to uncover a misunderstanding. Ouch. Low code can help, but it still takes time to design databases and paint screens. We’ve all been here before. Idea to Running: 1 Minute The marriage of GenAI technology with Logic Automation means you can now describe a system with a prompt: Idea to Running System: 1 Minute And get a complete running system in one minute  —  a database with test data, working screens, and an API. Even a fun little landing page: Landing Page from Prompt Admin App Admin App - Order Page We are moving veryrapidly. Logic Iteration  —  Natural Language: 1 Minute Now, our team can review the screens and identify changes. These might be structural (“customers have multiple addresses”) or logic (“it needs to check the credit limit”). So, we define “check credit” logic using declarative rules expressed in Natural Language and iterate: Natural Language Logic (instead of 200 lines of code) And now that’s running, also in about a minute. Suddenly, iteration cycles that used to take months are happening in minutes. The time wasted in misunderstandings  —  they still happen, of course  —  is inconsequential. We are moving very rapidly, iterating in the right direction. The logic above, while stated in Natural Language, is immensely powerful. It’s automatically ordered (simplifying iterations) and is automatically reused over all the relevant use cases. So, perhaps conceived for placing an order, our logic also understands how to process selecting a different product: Lookup Different Product - logic fires automatically to Check Credit Rules reprice the Order, but it exceeds the Credit Limit The five lines of logic above apply to about a dozen use cases. Without logic automation, this would be about 200 lines of code. That’s a 40X reduction, for the backend half of your system So, we’ve applied automation to all the elements of our architecture: the database, the screens, the API, and the logic. Simplicity Enables Your Organization The simplicity of GenAI  —  it’s just Natural Language  —  means we’re no longer limited to rocket scientists. Anyone can help get the requirements right. Organizational velocity: more people, moving faster, in the right direction. Dev Friendly  —  Model-Based, Standard Tools We are not claiming, by any means, that GenAI can automate complete enterprise-class systems. You will want custom front ends, integration with business partners and internal systems, etc. This means developers absolutely need to get involved. With… what? All we’ve provided is a few prompts… As you might have expected, a project has been created. It’s a standard Python project you can download and extend in your favorite IDE, completely configured and ready to run. Developers are cautious about generated “Franken-code”, for good reason. It can be monstrously difficult to understand, debug and extend. So, GenAI-Logic creates models, not code. The initial project creation included an API with OpenAPI doc: Automatic JSON:API - unblocks Custom App Dev, App Integration Because it’s a model, the API is four lines: Python as a DSL (Domain Systems Language): Executable API Model And here is the logic - five Python rules. It’s still at the same high level of abstraction, but you can now use code editors, debuggers, and source control: Logic Model - Logic is Automatically Invoked, Re-used, Ordered and Optimized Note the automation process did not just generate “raw” Python code. It would be around 200 lines. And we tried it: basic GenAI creates code that is poorly optimized, and wrong (it missed the corner case above). FrankenCode, at its worst. The ideal approach marries GenAI with Logic Automation engines, to execute the models for UI, APIs and Logic. Models - Not FrankenCode You can also provide the code as required. You have access to all the underlying standard tools (your IDE and its debugger, GitHub, etc.) and frameworks. For example, you can add custom endpoints, such as APIs for B2B partners: Custom Endpoint - a dozen lines of code (re-uses shared logic) Logic = Rules + Python. For example, to publish Kafka messages, define an event and a Python handler: Rules Are Extensible With Events - Send Order to Kafka And most importantly, your automated API is available: UI developers can use it with AI-dev tools, low code tools, or traditional frameworks. The logic has already been factored out and shared so they can focus on the presentation. No more business logic in buttons  —  that’s a bug since it prevents sharing logic between apps and services.App integration can use it to read and write data, subject to security. Since the JSON:API enables callers to select fields and join exactly what they need, custom API dev is dramatically reduced. Standards-Based Architecture and Deployment Runtime Stack - Automatic Rule Execution Containers are stateless for scalability. Deploy them anywhere. No fees apply. And There You Have It The combination of GenAI and Logic Automation means business users and developers can collaborate to create modern API-based systems that are far faster and far easier than ever before. Including declarative business logic: Create and iterate with Natural Language prompts.It’s automation. Developers can live with models, not code, familiar tools for customization and deployment.Integrate with partners and existing systems. You can try this out for free on your browser — visit https://www.genai-logic.com/. It’s all open source.

By Val Huber CORE

In the hustle to deliver value, development teams can unknowingly create software architectures that hinder their long-term progress. Initially, everything seems fine — features are shipping, continuous integration (CI) jobs are passing, and the product actually works. Yet, beneath the apparent progress, unchecked complexity can take root. What starts as manageable interactions between components can evolve into tangled dependencies and sprawling systems that hinder agility and slow innovation. Overly complex flows and unnecessary dependencies escalate complexity and architectural technical debt, creating barriers to maintainability and leaving teams vulnerable to setbacks at the most inopportune times. Overly complex flows and unnecessary dependencies escalate both complexity and architectural technical debt, hindering maintainability and leaving teams exposed to setbacks at the worst possible times. This article explores five subtle warning signs that your software architecture might not be as robust as you think. These patterns are: Dependencies Everywhere: A tangled web of dependencies that makes scaling and updating a nightmare.Your Architecture Isn’t as Clean as Your Code: The illusion of strong foundations.Your Codebase Is a Black Box: Lacking the visibility needed to manage and improve the system effectively.No Governance, No Control: Without rules and oversight, complexity spirals out of control.You’re Living in a Complete Mesh: A chaotic mess of communications that makes debugging and decoupling difficult. By contrasting these pitfalls with proven architectural practices, this guide offers actionable insights to help you regain control — or never lose it to begin with. You’ll learn how to reduce dependencies, foster architectural observability, and establish a governance plan that enables — not stifles — innovation. Along the way, we’ll demystify common anti-patterns and show how tools like Micrometer, vFunction, or Dash0 can play an important role in taming architectural debt. 1. Dependencies Everywhere Bad Architecture Dependencies are the hidden traps of software architecture. When your system is littered with them — whether they’re external libraries, tightly coupled modules, or interdependent microservices — it creates a tangled web that’s hard to navigate. They make the system difficult to debug locally. Every change risks breaking something else. Deployments take more time, troubleshooting takes longer, and cascading failures are a real threat. The result? Your team spends more time toiling and less time innovating. A classic example is the "domino effect," where a single update to a shared dependency causes unexpected failures across multiple services or areas of code. A classic example of this occurs when microservices are so interdependent they must be deployed together, defeating the purpose of their separation. This level of fragility stifles scalability; it makes your system brittle under the pressure of modern demands. Good Architecture A good architecture minimizes dependencies by prioritizing modularity through loose coupling. Internal components are isolated and should be communicated through well-defined APIs, while external libraries are carefully managed. This makes the codebase simpler, testing faster, and deployments safer. Modularity also allows for easier replacements or updates when a dependency becomes outdated or redundant, keeping technical debt under control. Good design principles, such as Domain-Driven Design (DDD) or the Dependency Inversion Principle, ensure that systems remain adaptable while minimizing entanglement. A clean architecture gives developers the confidence to work independently without fear of accidentally breaking the system. But Be Careful... Reducing dependencies doesn’t mean eliminating them entirely or splitting your system into nanoservices. Overcorrecting by creating tiny, hyper-granular services might seem like a solution, but it often leads to even greater complexity. In this scenario, you’ll find yourself managing dozens — or even hundreds — of moving parts, each requiring its own maintenance, monitoring, and communication overhead. Instead, aim for balance. Establish boundaries for your microservices that promote cohesion, avoiding unnecessary fragmentation. Strive for an architecture where services interact efficiently but aren’t overly reliant on each other, which increases the flexibility and resilience of your system. 2. Your Architecture Isn’t as Clean as Your Code Bad Architecture Focusing solely on metrics like code quality scores or deployment frequencies can create an illusion of success. Recent AI tools can produce perfectly formatted, error-free code, but their outputs often don't address deeper architectural flaws. Systems riddled with circular dependencies, tightly coupled services, or inconsistent business boundaries accumulate technical debt that AI tools can’t fix. Organizations chasing quick fixes with AI often find their systems becoming increasingly unmaintainable. Over time, the lack of governance and oversight in architectural evolution leads to slower delivery and escalating costs. Your code may look clean in isolation, but the holistic system it constructs becomes a brittle, tangled mess. Good Architecture Good architecture goes beyond code cleanliness to focus on the overall health and scalability of the system. Organizations cannot focus solely on metrics, but should instead shift to tool-driven governance that measures architectural quality in real time. Tools like vFunction’s architectural observability platform enable engineering teams to: Visualize, document, and validate your distributed architecture versus your design.Identify architectural drift and overly complex flows with every release.Map dependencies and enforce modularity, patterns, and standards.Prevent unnecessary complexity with clear, actionable tasks. Dash0 offers similar visibility into existing architectures. There exists open-source tooling in many programming languages for generating class diagrams and their connections, such as Pyreverse for Python. High-performing teams focus on building systems where clean code is complemented by clean, well-governed architecture. But Be Careful... Avoid overcorrecting by implementing governance so rigidly that it stifles innovation. Architecture must evolve alongside business needs, and overly prescriptive rules can slow development or discourage experimentation. Instead, governance should act as a guide, not a constraint. Use tools like SonarQube, vFunction, or Micrometer to find the right balance, providing visibility and insight without micromanaging or focusing on rigid metrics like 100% code coverage. Remember, the goal isn’t perfection — it’s to maintain flexibility while keeping technical debt and complexity in check. By treating architecture as an evolving, observed aspect of your system rather than a one-time effort, you can ensure long-term maintainability and foster innovation without sacrificing quality. Clean code and clean architecture go hand in hand to support sustainable software development. 3. Your Codebase Is a Black Box Bad Architecture A black-box codebase is a silent killer of productivity. If your development team can’t easily discern the structure, dependencies, or behavior of your system, every change becomes a high-stakes gamble. Debugging turns into a wild goose chase, adding new features requires navigating a labyrinth of unknowns, and scaling becomes guesswork. The hallmark of this problem is a lack of architectural clarity: sprawling code, undocumented interdependencies, and complex behaviors buried deep in legacy systems. Teams often rely on tribal knowledge — only a few senior developers truly understand how things work, and when they leave, that understanding goes with them. The result? Sluggish development, fragile systems, and technical debt that compounds exponentially. Good Architecture Good architecture prioritizes transparency and understanding. Tools such as vFunction or Dash0 can bring your architecture out of the shadows and into the light. Both of these offer architectural observability capabilities that help visualize your codebase, revealing service boundaries, interdependencies, and areas of inefficiency. With vFunction, teams can document complex microservices in real time, making it easier to spot bugs and bringing architecture-as-code to life with support for tools like Mermaid for sequence diagrams and automatically validating the actual architecture against manually created C4 diagrams for system modeling. This visibility empowers teams to tackle architectural debt head-on. By identifying tight coupling, outdated modules, or unnecessary complexities, you can make informed decisions about refactoring, modularization, and scaling. Additionally, a well-documented, well-understood system reduces onboarding time for new developers and ensures that even complex projects remain manageable over time. But Be Careful... Visibility alone doesn’t solve the problem. Knowing where the debt is without addressing it is like diagnosing a broken engine but refusing to repair it. You need to act on these insights. Refactor tightly coupled components, consolidate redundant code, and reduce unnecessary dependencies. Moreover, visibility must be paired with a governance framework to prevent the same issues from re-emerging. Establish architectural guidelines, enforce coding standards, and implement automated testing to ensure your system remains maintainable and scalable as it grows. Good architecture is a continuous process, not a one-time effort. 4. No Governance, No Control Bad Architecture Without strong architectural governance, even the most well-intentioned teams can inadvertently create chaos. In the absence of clear standards and oversight, silos form. Each team develops its own solutions — reusing patterns inconsistently, integrating new technologies without alignment, creating black boxes, or duplicating efforts across projects. Over time, this leads to a patchwork architecture riddled with redundancies and complexity. This lack of coordination can result in architectural sprawl, where microservices balloon out of control with tech debt. Worse, diagnosing and resolving problems becomes an uphill battle, as there’s no shared understanding or cohesive strategy to guide the way. Good Architecture Governance is the glue that holds your architecture together. A strong governance program establishes standards, guidelines, and best practices that teams can follow to build systems consistently and efficiently. It ensures that every architectural decision aligns with broader business goals and prevents unchecked sprawl of microservices. With vFunction’s architectural governance solution, you can automate the detection of architectural drift, helping to ensure that teams stay aligned with established patterns. By offering real-time insights into your system’s design, vFunction empowers organizations to monitor and enforce architectural standards, including ensuring authorized service communication and safeguarding correct database-service interactions without manual intervention. Additionally, consider creating Architectural Decision Records to document decisions and the discussions leading up to them within your team. Architectural governance isn’t just about preventing chaos — it’s about enabling scalability and resiliency. With clear guidelines in place, developers can focus on delivering value while staying within guardrails that promote maintainability. But Be Careful... While governance is critical, it shouldn’t come at the cost of agility or innovation. Overly rigid governance programs can lead to bottlenecks, slowing development and discouraging experimentation. Developers may feel constrained and start finding ways to work around the rules, which only exacerbates the problem. The key is balance. Governance should provide structure without stifling creativity. Communicate architecture decisions with Decision Records. vFunction’s governance tools can excel here by offering dynamic oversight that adapts as your architecture evolves. By integrating automated checks and real-time metrics, vFunction ensures that governance is lightweight and non-intrusive while still maintaining control. Think of governance as a way to provide developers with a clear, shared map — not as a way to limit where they can go, but to help them navigate smarter and faster. 5. You’re Living in a Complete Mesh Bad Architecture A service mesh — where services are excessively interconnected and dependent on one another — can paralyze development. In this anti-pattern, communication paths between services become so complex that even small changes or failures in one part of the system can trigger ripple effects across the entire application. For example, imagine a service that must call five other services to complete a single transaction, with each of those services relying on yet more downstream calls. Latency escalates, redundancy increases and operational complexity is high. The result is a system that feels more like quicksand than a foundation for innovation. Good Architecture Good architecture avoids the mesh by focusing on clear, well-defined service boundaries and minimizing inter-service communication to what’s absolutely necessary. Services should have specific, cohesive responsibilities, ensuring they operate independently as much as possible. Use asynchronous communication mechanisms, such as message queues or event streams, to decouple components. This approach not only reduces direct dependencies but also improves fault tolerance, allowing services to operate even when upstream or downstream components are unavailable. Additionally, designing communication to flow in a one-directional manner (wherever possible) simplifies dependency management and ensures a more predictable system. Again, vFunction can provide a critical service by identifying and untangling these circular dependencies. With its ability to map and analyze dataflows, vFunction provides a visual representation of how services communicate across your architecture. Other tools for specific languages, such as JavaScript, use tools like ES Lint or Madge to detect circular dependencies. For Python, Pyreverse excels at visually representing circular dependencies of modules by highlighting circular communication routes, excessive communication, and tightly coupled components. From there, these insights allow teams to take corrective action. But Be Careful... Just as over-communication is harmful, so is excessive isolation. Completely isolating services without a clear business need can lead to redundant work, data inconsistencies, and an inability to effectively share critical functionality. The goal is to strike a balance — reduce unnecessary dependencies while ensuring services collaborate when required. ES Lint, vFunction, and others can help you achieve this balance. These insights ensure that your architecture remains efficient and maintainable even as it evolves. Think of your services like a network of highways: traffic should flow smoothly, without unnecessary intersections or dead ends. With the right tools and principles, you can avoid the pitfalls of a complete mesh and build an architecture that is both scalable and resilient. Conclusion The success of your product depends on its ability to support your organization’s goals without becoming a bottleneck. In this article, we explored five common signs of bad architecture, dissected their impact, and demonstrated actionable steps to address them. By focusing on reducing dependencies, balancing metrics, ensuring visibility into your codebase, establishing effective governance, and preventing complete meshes, you can transform your architecture into a strong foundation for innovation. Have a really great day!

By John Vester CORE

Traditional monitoring is not enough. We need developer-centric solutions that only Observability 2.0 can give us. Read on to see why. Beyond Traditional Monitoring In today's software development landscape, creating cutting-edge applications requires an acute focus on the developers who craft these digital experiences from start to finish; henceforth, contemporary tools are not just expected but demanded to be inherently developer-centric-offering environments that promote efficiency and creativity. Observability 2.0 goes beyond traditional monitoring paradigms by embedding a continuous feedback mechanism directly into the development lifecycle itself rather than as an afterthought or separate process, demanding transparency across every phase of software production to maintain system health at all times while ensuring that code quality and application performance adhere strictly to enterprise standards. This modern approach mandates developers work within a cohesive platform ecosystem where debugging tools are tightly integrated with the IDE. This immediate access allows for quick identification, analysis, and resolution of issues directly from their coding space without extraneous context switching or external dependency on legacy systems often associated with traditional observability setups. Additionally, modern developer-centric solutions facilitate real-time collaboration through shared canvases where developers can visually track system states alongside peers. This not only streamlines the debugging process but also enhances knowledge sharing and collective problem solving which is pivotal for developing complex software systems that are robust against failure. Telemetry Is the Key Observability 2.0 demands a sophisticated layer of telemetry capture, where metrics related to performance (response times), throughput (transactions per second), resource utilization (CPU and memory usage) as well as log files from each unit test or integration phase are all recorded with precision. This data is then processed using advanced analytics tools that provide a granular view of system health, enabling developers to proactively pinpoint the root cause before problems escalate into critical failures. Furthermore, more than just flagging issues when performance dips below acceptable thresholds, these solutions incorporate machine learning techniques for predictive analysis. This means identifying patterns and potential future risks based on historical data, which in turn allows developers to iterate with an awareness of possible scaling concerns or resource bottlenecks. This next-gen observability approach also integrates into the Continuous Integration/Continuous Deployment (CI/CD) pipelines. By doing so it informs build automation and deployment strategies, ensuring that only applications with verified metrics pass through to subsequent stages of testing or release. Developers are empowered by dashboards within their workflow which highlight the health status of different modules; these visual indicators provide clarity on areas in need of immediate attention thus enabling an accelerated development cycle while keeping developers abreast without distracting them from writing quality code under real-time conditions. To truly be modern and developer-centric, observability solutions must also incorporate robust logging mechanisms that allow for tracing execution flow. This granular detail in log files becomes essential when debugging complex application flows or distributed systems where component interactions can lead to obscure interdependencies causing unexpected errors. Advanced monitoring tools now provide contextual information about these logs while still within the developer's environment, thus not only facilitating rapid issue resolution but also allowing for a deeper understanding of how code elements interact throughout their lifecycle. This insight is critical when developing with microservices or serverless architectures where traditional observability techniques may fail to capture subtle inter-service communication nuances. Moreover, Observability 2.0 in the context of developer tools means implementing end-to-end trace visualization capabilities so that developers can comprehensively understand how their code interacts with various system components. This is not only about pinpointing issues but also validating design choices; for example, understanding latency between API calls within a service mesh or tracing data flows through multiphase transactions. Integration With Developers’ Tools Integration of developer-centric observability tools into the daily workflow requires careful planning and thoughtful architecture that supports various testing environments. This may range from unit tests to endurance runs in production replicas, ensuring that monitoring is not just an afterthought but a pervasive element throughout development. It becomes part of their armor as they write code; visualization dashboards are embedded within IDEs or dedicated developer hub applications enabling immediate insights into the behavior and health metrics at all times. This transparency builds trust among teams, fostering an environment where developers can confidently push new features without fearing that a bug introduced today could cause tomorrow’ extraneous distractions. Modern solutions must also facilitate observability in containerized or cloud-native environments which are becoming increasingly common. This means adaptive tools capable of spanning across multiple infrastructure layers whether it's monitoring containers, Kubernetes pods, serverless functions, and beyond — each layer offering unique challenges but equally demanding precise telemetry collection for effective observability. Developers should leverage these modern solutions to not only maintain the high performance expected by end-users today but also architect futureproof systems that can rapidly scale without compromising on reliability or stability during sudden traffic surges, all while retaining their focus on writing clean and robust code where developers are empowered through observance of how every line they write impacts overall system behavior. In summary, a modern developer-centric approach to Observability 2.0 insists on integrating real-time analytics into the development process for maintaining software health. A multiprong strategy encompasses embedding debugging tools within IDES offering immediate feedback and collaborative canvases that align with contemporary cloud workflows, incorporating advanced metrics processing in CI/CD pipelines, adopting comprehensive logging to trace execution flow through complex application structures while providing end-to-end visualization for full contextual understanding of code interactions. Modern software development demands these solutions not just as optional but as core components driving efficiency and precision: the bedrock upon which developers construct systems that are resilient, performant, and scalable, maintaining fidelity to enterprise standards while fostering a transparent environment for rapid iteration leading towards the ultimate goal of high-quality software delivery. Observability 2.0 Is a Must In conclusion, embracing developer tools with Observability 2.0 in mind is no longer optional but rather an imperative element. Developers today require these advanced features as part and parcel of their everyday coding practice just like they would rely on any other essential toolkit such as version control systems or build automation. Modern solutions must evolve beyond conventional boundaries, becoming intrinsic aspects of a developer's environment where each keystroke is informed by real-time metrics that influence immediate decisions and promote an enriched understanding. This harmony between coding fluency and observance ensures not just delivery but also sustainability in today’s ever-evolving landscape.

By Adam Furmanek CORE