Testunity

Most businesses want software and applications with top-notch quality in quick time. For that to happen, test automation is the key and rightly so. In fact, it has now become the pivotal point for any test cycle. It has also improved the scenario considerably, as testers can now have more time to perform more valuable work like coming up with innovative ideas to make the final product better or performing tests that definitely need manual supervision.

The integration of AI and ML in test automation has further improved the results. However, as a direct result of swift completion, businesses and product owners want high quality products. Products that not only please the customers but also make their lives easier for real are the ones that stand out. This is where applying data analytics to test automation can change the game.

The need for Data Analytics

It is the era of Big Data and Data Analytics. This will change how automated testing is done for sure. The aim now is to create test cycles based on the insights from the previous tests to get an improved product in the end. Creating data-driven tests always helps the cause, as are typically created in accordance with the patterns and the behaviour shown by the past tests, thus avoiding mistakes.  

However, the process on the whole is not just about creating automated test cases for building an application or software but also for updates for the same. When it comes to creating updates, the data you collect about the performance of the existing version matters the most. Software companies around the world are now coming up with regular updates and they use data from test cases based on the user experience. For example, Google launched eight updated versions of Chrome in 2018 alone.

For all this, you need customer-driven data regarding the way each feature is being used. Data Analytics helps you get a much better picture of the things happening in the real-world scenarios.

This is even more important in an era where DevOps Testing methodology is widely adopted by testers. In this method, continuous testing is the key to get the desired results in quick time. Such a continuous delivery pipeline requires constant feedback for each developmental cycle, referred to as the ‘feedback loop’ in the testing world.

Since a majority of the tests are automated in DevOps, the need for data analytics become a lot more important than it could have been for a Waterfall methodology.

The current situation of Test Automation

Ever since the Agile and DevOps methodologies have become the norm among software development and testing teams around the world, test automation has become incredibly important. However, it has not been able to do the job of providing continuous feedbacks, which is necessary in the current times. This is mainly because of the fact that automated tests are only considered to be something that increases the velocity of the output in the end.

However, this is a very limited view. Test automation has much more to offer if you apply data analytics to it.  If you can use all the data collected during and after the initial tests are conducted, you can analyse all of it to get a better insight on performance. You can then streamline your efforts in the future in accordance with that. With the thorough recording and analysis of the data, you can remove redundant tests from the test cycles.

If you want to leverage the real potential of test automation, you will have to complement it with continuous regression testing and that can only be done efficiently if you apply data analytics to it.

How can Data Analytics improve Test Automation?

It is incredibly important to retain all the test execution results so that you can take valuable insights from them to understand the overall scope and health of your product. What usually happens with the execution results is that they are used to create test reports and then are forgotten. Instead, you should store such data in the centralised database. You can use all the data about the actual scripts, test cycle or iteration, execution results with logs, product release and so on for your own benefit.

Automated tests can also generate machine data like event logs, device vitals and server parameters – all of which can be used as measurements to create new test cases. Furthermore, data analytics can also help you with information on how many times changes were made in the codes, who made the changes and so on. You can get specific API tools to identify such data nuggets.

With the extensive analysis of the test automation history, you can help yourself in the following ways:

  • You can identify and exclude flaky tests with the help of data analytics. Every test cycle created by each test team has several flaky tests that pass and fail for the same features. Once you identify such tests, you can avoid them in future runs, as they take a lot of execution time and hence increase the maintenance costs. You just need to perform test series analysis and identify such tests and not perform them until they are fixed.
  • When you make an update, more often than not, the whole system does not get affected. Therefore, there is no point in performing the tests for all the functions. This is where data analytics can help you have a clear picture of the areas that get affected by an update, so that you can run tests just on them. In a nutshell, it helps you exclude the unaffected subsystems from the new test cycles, thus saving you both time and money.
  • You can identify the impact of codes generated by a particular author in your test cases. Everyone knows that no two coders are same, with some being more prone to bringing in bugs to the systems. With the data collected from the source change history sheet, you can identify the changes brought by a particular author and how it has impacted the overall product. This way you can improve your team by either hiring a new tester or by improving the skillset of the existing tester.

Collecting various data points and insights from your test results will only help you in improving your test cases. Furthermore, you can also predict the behaviour of the product and test cases in future by using the right predictive analysis tools, which also comes under the umbrella of data analytics.

Now that you know how crucial data analytics can be while testing your software, you should hire a company like TestUnity that follows this methodology in their testing cycle.

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