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If you have introduced test automation as a means to achieve more efficient testing, you’re probably also interested in making the test result analysis process as efficient as possible. This requires the right approach and the right set of tools.
Test automation drives high levels of productivity, reduces risk, and lowers costs, which is why automation is increasingly being adopted by testing teams and QA.
Once tests are automated, they are completed at higher speed than before, which, unsurprisingly, means the number of test results also grows. Over time, as sprints are completed and the software is developed, the number of automation test cases accumulates, reaching into the hundreds or even thousands.
The challenge then becomes how to manage and analyze the vast number of test results at the same speed they are generated.
In this blog post, we will guide you through test automation test result analysis in X steps, so that you can get a clear and thorough overview of why your tests are failing in the shortest time possible.
Analyzing test results in software testing can be a tedious task. Without the right tools at hand, it can be extremely difficult, if not impossible, to uncover why a test failed and how to fix it.
It goes without saying that when testers start spending more time trying to understand test automation results than they do on testing, the benefits of automation start to fade.
For this reason, a structured approach is critical. Before we dig into how to approach test result analysis, however, it’s important to clarify exactly what is covered when speaking of analyzing test automation results.
There are several aspects to analyzing test results:
In the following, we will cover each of these.
The first aspect - checking if automated tests are built correctly, and testing what they were intended to test - is essential for success with test automation in general. If your tests aren’t set up right, how can you rely on your test results?
There are many best practices for setting up test automation and building test automation flows, too many for the scope of this guide.
There is one thing, however, that makes all the difference when it comes to ensuring the accuracy of your tests, and that is how easily you and your testing team can decipher what your test cases are actually testing.
By using no-code automation that presents your automated tests in an intuitive, visual way rather than in lines of script, it becomes much easier for everyone to catch a glitch in a test flow.
When building your automated test flow, smart recorder functionality is ideal for UI tests, as it simply replicates the user’s actions and builds the test flow based on those exact actions. This leaves little room for incorrectly built tests.
For tests that fail anyway, some tools offer the capability of naming the purpose of each step in the test flow in an easily understandable way, so that any tester can quickly identify where the glitch might have occurred.
The next aspect in test automation result analysis is reviewing the individual test results to understand why any failed tests have failed. This is often the task that testers spend much of their time on, which can result in a lot of frustration. Luckily, there are a number of things you can do to mitigate these frustrations.
Any test team already has plenty of tasks as part of the software delivery process, so simply adding another task of monitoring a result log, does not necessarily result in quality improvement.
Having a test team constantly monitoring tests results on their own comes with several risks, for example:
Instead, make sure that the tool used for test automation allows you to set up alerts that send out messages when the test team needs to act, e.g when one or more test cases fail or when the execution of a test case takes longer than a predefined critical limit.
By setting up automated notifications like these, testers can react when needed to check in on the automated test cases, and not waste time by stating that nothing has failed.
If a tester spends more time analyzing why an automated test case has failed than it takes to execute the case, automation loses its purpose. Investigating a failing test case and pinpointing the reason for failure shouldn’t be difficult. Product owners, developers, and testers need swift feedback to catch irregularities as fast as possible.
A test automation platform should therefore include the following features to help testers be more productive in the analysis phase:
Release platforms such as Quality Center, Jira, and TFS can be used for both managing tests and handling bugs. They are widely used among test teams as tools for keeping track of bugs, test strategies, test case descriptions, and more.
Introducing test automation probably won’t change the fact that these platforms serve as the center of collective testing efforts.
This is why you should integrate your test automation platform by either pushing results to the test management system or pulling results from the test automation platform using an API.
Fast and transparent feedback is a cornerstone in DevOps. Continuous feedback loops allow the development team to react to issues quickly and fix them before a bug is released into a production environment.
An effective way to share results in and between teams is to use visual dashboards on shared monitors in a team’s work space. For example, showing a simple graphical representation of the latest results from regression tests on the test environment will give the team a clear indication of the current quality of the software under test.
The last aspect in test automation results analysis is about looking at test automation results from a broader perspective, and gaining a more in-depth understanding of test failure than with individual test case analysis.
When test automation suites grow big enough and the product becomes increasingly complex, more failed tests are bound to occur. In this case, it can be an advantage to start looking at tendencies in failed tests, in order to learn how to better prevent them over time.
A straightforward way to visualize results at scale is therefore critical. Vendors such as Power BI and Tableau are ideal for this, as they are designed to give you a powerful yet clear way to look at the big picture.
You can monitor and analyze outcomes of test automation at scale, understand which tests fail, and look at execution failure trends to more easily identify errors in test build or in the application under test.
Advanced analytic capabilities also give you the additional benefit of being able to manage large deployments and track who is doing what across teams, which is an essential component of agile development and DevOps.
A test automation tool that enables you to get an intuitive, graphical and comprehensive overview of test outcomes as well as a granular understanding of individual test failures is therefore important.
Leapwork is a tool that make test automation result analysis easy and efficient.
Leapwork's no-code test automation platform enables you to set up, maintain, and analyse test results with ease and speed.
Learn more about the Leapwork platform in our webinar on no-code test automation.