It was some years ago. My friends developed a startup project. They planned to publish the first version of the application as a full-fledged product with limited but very handy functionality. They announced the day of publication in the media about startups and social network profiles. Moreover, on the same day, they planned to launch the project on Product Hunt to attract the first professional audience.
Investors, potential partners, and even competitors knew when the product will be released. Everything was ready. However, after the publication of the application, it turned out that the software worked just on some single devices. It was a failure. Especially when the team understood that it was because of the bug they met for the first time some months ago. This situation would not have happened if my friends had done the failure analysis. Today, Zebrunner team explains why failure analysis is important, what components it has, and how to simplify test failure analysis.
Why do we need to analyze test failures?
Quality assurance is one of the key elements of software development. At the same time, QA could be a bottleneck when bugs at the final stage of development delay your product release. To escape such a risk you perform a test failure analysis.
A test failure analysis is a procedure for exploring the reasons why your test failed. It allows you to identify the root causes of your bugs, fix them, and boost your testing workflow.
I will share an example from my previous work to explain why it’s important to do failure analysis. Some years ago I was the main editor of a website. My KPIs were connected with the traffic that my articles attracted. After the first month of work, I was disappointed to find out that traffic had fallen. Nevertheless, I didn’t give up and analyzed the most popular articles and unpopular ones. As a result, I put forward a theory that the root cause of weak traffic was boring headlines. I checked this suggestion and got good traffic!
There are in the QA sphere you even don’t need to spend your time looking for root reasons. Smart test automation reporting tool does it for you. In-built AI/ML technology analyzes all your failed tests and automatically identifies the root causes. Customizable widgets and dashboards provide you and all project stakeholders with insights. Test artifacts help you to give fast and detailed feedback to developers.
Basic components of failure analysis
However, initially, you need to understand how failure analysis works. Here we explored its key components.
Software & testing issues
When you launch a test run and detect some bugs it doesn’t mean that the reason is the problem with the code. Yes, it’s possible, but you also can have automated script issues or problems with the environment as well. So you need to know exactly what the failure reason source is to enhance your QA process.
Root causes detection
Suppose, you run automated scripts and some tests fail for similar reasons. The error is that some web pages haven’t opened. The root cause here can be hidden in the server or environment. So, we report to developers about the root cause. By fixing it we get the list of issues solved. Otherwise, when we send any private issue to developers, we get just private bugs fixed. This doesn’t guarantee you will not encounter the same bug again. This leads to release delays and team time and cost overruns.
Bug importance
When we know whether our failure is hidden in software code or the environment or in test automation script and what is the root cause of this failure, we need to determine its significance. This information helps us to decide when it’s better to fix the bug.
For some types of errors it’s better to fix them in the latest stages of development. At first glance, it could seem a bit strange. However, there are some examples. First, you have a spelling mistake in a pop-up on your website. Before the release, the message could be changed several times. Moreover, this mistake can’t impact functionality, UI & UX. So it doesn’t make sense to fix it as soon as you detect this error. Second, you have recognized a bug in separate functionality that can’t affect another functionality. At the same time, changes in other functionality impacts this feature. In this case, it’s better to make all changes with high-level functionality and just after that fix low-level features.
Failure scope
This component allows us to identify what is the possibility that we can meet the same failure in other application configurations or builds. This is a good approach for QA teams who can’t test every possible environment. By analyzing your failure scope you can understand in which cases your software can face the same issue.
Test failure analysis approaches
There are some approaches to test failure analysis performing. We have explored two of the most effective.
AI/ML failure classification
Many test automation solutions have in-built AI/ML failure classification features. This reduces test automation costs because you have reasons for failure and their root causes are automatically determined. Thus, you save your QA & development team time.
AI/ML models are learned pretty fast. After some period of training (usually, it depends on how much QA engineers work with the algorithm) the technology speeds up your QA process.
Smart reporting
Advanced reporting provides you with a lot of benefits in concerns of failure analysis.
Firstly, it gives us many test artifacts like logs, screenshots, and videos. These artifacts are extremely handy when you send your feedback to developers. With test artifacts, you ensure them with failure and with comprehensive information on where it happens and how it seems.
Secondly, with smart reporting, you get flaky test statistics. This way you gain insights about how to ensure the maximum possible product quality.
Thirdly, reporting guarantees you comprehensive data about product quality. You can give instant feedback to developers and share test results & statistics with stakeholders. It simplifies and speeds up the software development life cycle.
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