Automated Testing Using Jenkins and Python
Automated Testing Using Jenkins and Python
See how to use Jenkins and Python to reach optimized automated testing, including points about plugins and integrations.
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Software projects, whether created by an entire team or an individual, use continuous integration (CI) to make sure that essential steps such as unit testing are automated rather than being a manual process. CI can dramatically reduce deployment times by automating steps that require human interference, like testing. A variety of free and open source continuous integration servers are available as per the needs of a project. Jenkins is a common CI server for building and deploying into test and production servers. It is often used for Python projects because it is open source and programming language agnostic, while the Python Test Framework works as a test automation tool to manage and distribute the execution of web-based functional testing. It provides concise idioms and uniform testing techniques.
Challenges in Automated Testing
Automated testing is definitely not a substitute for good testing practices, and it cannot alter the “physics” of testing. In fact, it cannot guarantee the absence of bugs, nor is it a guarantee of a quality product release. Even with automated testing, the challenges faced are
1) Understanding Company, Client, and End User Expectations
The expectations of end users can often differ greatly from those of the developers, so understanding customer feedback can often be one of the most essential and challenging things a software developer has to do. Testers also have to learn from this customer feedback, and thus build better scripts to map actual user interactions with greater precision.
2) Clear Communication Within the Organization
This sounds like a usual challenge, but it is a must when it comes to efficient working and testing. Imagine the scenario of an organization when more than fifty features releases and how the lack of clear communication can quickly turn into a production nightmare. So, between testers and the rest of the development process, there are things one needs to do to simplify the process.
3) Proper Use of Testing Resources
It requires a technical mind to properly build, maintain, and understand the reporting of test automation. Developers can manage, but they aren’t trained testers, just as testers can’t write code as they aren’t really developers. So, the process of sorting the right source for an efficient output is a need.
4) Keeping Automation Test Cases Up to Date
With multiple development cycles adopting an agile approach, the automation has to provide up-to-date test cases as quickly as possible. For those who consider automation as a set and forget for software testing, there will always be some maintenance required on the automation scripts.
5) Selecting the Right Tool
With the variety of options, ranging from free and open-source testing tools available like Selenium, each tool tends to support particular situations. No matter what kind of process and testing methodology are made available, if it does not match the technical and business expectations, the test automation will fail, so choosing a test tool is the complicated procedure.
6) Investment Cost
Automated regression testing is crucial and useful in most Agile contexts. But when turning to the cost, the initial phase of test automation is usually expensive. Although there is a gigantic payoff in the long run, convincing the stakeholders to reach a consensus about this investment is a big challenge. Actually, just due to budget constraints, people tend to give up on test automation.
Optimizing Automated Testing With Jenkins
Jenkins is a great tool for continuous integration and continuous delivery. To obtain an optimized automation solution, quality objectives need to be taken care of, such as:
1. Jenkins Should Be Stable and Work Correctly
Jenkins releases new versions frequently. It’s suggested at all times to use the latest Long-Term Support (LTS) release and to avoid the updates in-between. Backups of the Jenkins server should also be created, and if it costs a lot of time and resources, one can back-up the config files through scripts like this.
2. Don’t Fill Jenkins With Bloat
One can create multiple Jenkins masters if there are a lot of teams working on the varied projects. This will ensure that changes don’t affect all projects and that one has only the plugins needed on a specific master for each project.
3. Automate Jenkins
Jenkins comes with an incorporated script console to execute instructions on the server. It’s a versatile tool to debug Jenkins or to locate the information required. An interesting plugin is the Scriptler plugin. With this plugin, one can save the preferred scripts for later reuse.
4. Carefully Consider the Plugins
It’s important to decide if the given plugins are needed or not, as Jenkins has a rich collection of plugins and it’s easy to get lost. It can potentially break functionality where one might not expect in Jenkins. So pick the right plugin for the right job and uninstall any plugins which are no longer used. This will help to keep Jenkins in good shape and free of bloat helping in better testing.
5. Integrate With Other Tools
Jenkins offers a REST API, which can be used to integrate Jenkins with other tools. Perhaps one of the best integrations is with source control. Each time a developer creates a pull request to the source code, Jenkins can be triggered to check the pull request for any flaws. Jenkins can also be integrated with a code review tool, such as Gerrit. It can be used to verify if all the functionalities are as required. Jenkins integrated with bug tracking systems like Jira and Redmine can post updates in these bug tracking tools when building a developer’s fix, allowing a higher level of visibility across the team and notifying other team members when an issue is fixed.
6. Set Up the Correct Slaves
Make sure to have easy-to-manage slaves. Make sure one can replace them or add new ones without a hassle. If a slave crashes, make sure it only takes a couple of clicks to get a new one up and running.
Optimizing Automated Testing With Python
Optimization is premature if the code is not working and is also premature if the code is working, but one is not satisfied with the overall architecture and design. The following things can be done to optimize testing.
1. Interning Strings for Efficiency
Interning a string is a method of storing only a single copy of each distinct string. Python has a lot of internal code using dictionaries, which leads it to perform a number of searches for identifiers; interning the identifier strings speeds up the whole process. This optimization takes place during compilation, so it’s quite a useful feature in Python.
2. Peephole Optimization
Peephole optimization methodology optimizes a small segment of instructions from a program or a section of the program. It helps in spotting the instructions that can replace with a shortened version. It has a built-in way of doing it.
3. Using Generators and Keys for Sorting
Generators are a great tool for memory optimization. They facilitate to create functions that can return one item at a time instead of returning all at once.
4. Optimizing Loops
Python has a way of turning loops to perform faster. It has a couple of building blocks that support looping. The Python engine expends considerable effort in interpreting the for loop construct. Hence, it’s always preferable to replace them with built-in constructs like Maps. The level of code optimization also depends on the knowledge of Python's built-in features.
5. Avoid Using Globals
Python is really slow at accessing external variables, so it disapproves the excessive or unplanned use of globals. One can declare an external variable using the global keyword and make a local copy before using them inside loops.
6. Use Built-In Operators
Python is based on high-level abstractions. By using built-ins, it makes the code more resourceful because it is pre-compiled and fast. It adds significant improvements in speed by using built-ins like Maps.
Jenkins is a great tool for continuous integration and continuous delivery processes. With Python, future changes and maintenance are easy to express in the overall structure of the program. If one opts for the more stable LTS versions, avoid bloat, spend a bit of time automating, choose the right plugins and the right integrations, and get involved in the community; the automated testing processes will really start to shine.
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