Applying Data Analytics for Intelligent Test Automation

Applying Data Analytics for Intelligent Test Automation

We live in a time where technology is forming itself in a variety of ways, with algorithms and machine learning playing a key role. It is now the time for Data Science and Analytics, which have proven to be beneficial in obtaining informative test findings. These findings can be carefully cultivated as actionable data for future development. If we can join the dots and better utilize data analytics, software engineers will be able to assess the efficacy of their test automation with greater confidence. Furthermore, they can easily track different metrics and parameters involved in the structuring of the test automation processes which are part of the automated software testing implementation.

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore, American organizational theorist

What is the Need for Data Analytics?

According to Market Research Future (MRFR), the global data analytics market size is predicted to reach USD 132,903.8 million at a 28.9% CAGR by the end of 2026. Primary factors accelerating the global data analytics market include the extensive implementation of advanced technologies focused on business operations and the adoption of AI and IoT.

Conventionally, the data we used was generally structured and small in volume, and it can be analyzed with basic business intelligence tools. Unlike traditional systems, when data was primarily structured, today’s data is mostly unstructured or semi-structured. This information comes from a variety of places, including text files, financial records, multimedia formats, devices, and sensors. Simple business intelligence tools are incapable of dealing with such a large amount and variety of data. And that is why, in order to process, analyze, and extract useful insights from it, we need more complicated and advanced analytical tools and algorithms.

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To be precise, data analytics allows you to gain a much sharper view of what’s going on in real-life circumstances.

While working with software and applications, the process is not limited to the creation of automated test cases, but also includes upgrades. When it comes to developing updates, the data you collect on the prior version’s performance is critical. Globally, software companies are progressively issuing regular updates based on customer feedback and test case data. Google, for example, released a total of eight new versions of Chrome in 2018.

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

Role of Predictive Analytics to Improve Testing

This branch of analytics employs mathematical methods and machine learning to predict the results of software testing operations. This method makes use of both current and historical data to create insights and pinpoint probable failure locations in software testing results. This allows development and testing leaders to resolve issues more quickly and easily by proactively addressing them early in the lifecycle. Predictive analytics can also aid in the detection of delays and errors during software testing cycles. It also aids in the monitoring of team productivity during human-assisted testing cycles. When software engineers employ predictive analytics in the testing process, they can also undertake risk mitigation activities.

How Data Science and AI will Improve Test Automation?

Test automation has grown increasingly crucial because Agile and DevOps approaches have become the norm among software development and testing teams throughout the world. It has not, however, been able to perform the task of delivering constant feedback, which is critical in today’s world. This is due to the fact that automated tests are only thought of as something that boosts the output velocity in the end.

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When data analytics is used with test automation, it has a lot more to give. If you can use all of the data gathered during and after the initial tests, you may evaluate it all to have a better understanding of performance. As a result, you’ll be able to simplify your efforts in the future. You can eliminate unnecessary tests from test cycles by meticulously capturing, and analyzing data.

On the other hand, machine learning trains systems to learn and apply what they’ve learned in the past, allowing software testers to produce more accurate outcomes than traditional testing could ever do. Also, the likelihood of inaccuracy isn’t the only thing that decreases. The time it takes to run a software test and detect a potential fault is also reduced, yet the volume of data that needs to be handled can continue to grow without creating any unnecessary hassle for the testing team.

Below mentioned are some of the improvements data science and AI can bring across test automation processes.

Test Creation

Data science systems may aggregate real-time data from many sources, detect hidden patterns, and generate new rare critical scenarios, all of which can aid in increasing product quality.

AI can create test scripts with the use of data supplied by Data Science Predictive Analysis. On this foundation, AI can begin constructing test cases based on real-world data. It is intelligent enough to group together frequently performed activities, like logging in and out of the application, into reusable components. So it injects our tests with these freshly constructed reusable components.

Test Execution

The time and effort required to create, and execute tests with the chosen tool or framework, as well as the availability of qualified resources, are two of the most major barriers stopping organizations from moving forward with automation. There are a few AI tools that can assist in resolving this issue. Tests that used to take weeks are now finished in a matter of hours. This is accomplished by the creation of reusable components, the use of fast-running tests, and the integration of CI/CD with multiple grids.

Moreover, as test length falls, test automation productivity increases and non-technical individuals may deliver automation testing services with the assistance of AI.

Track Changes

Automated testing can also create machine data such as server settings and device vitals, which can then be used to develop new test cases. Furthermore, data analytics can provide you with information such as the number of times changes to the codes were made, who made changes, and so on. Specific API tools can be obtained to identify such data chunks.

Reduce Flaky Tests

With the use of data analytics, you can discover and exclude faulty experiments. Each test cycle developed by every test team has a number of flaky tests which pass and fail for the very same features. After you’ve identified such tests, you can skip them in future runs because they take a long time to execute and hence raise maintenance expenses. You only need to run test series analysis to detect such tests and refrain from performing them until they are corrected.

Self-Healing Tests

Root cause analysis (RCA) identifies all possible causes of test failure and directs you to a route for one-click fixes.

Identify Impact of Codes

It is a well-known fact that no two application developers are alike, with some being more prone to introducing defects into systems than others. You may identify the modifications made by a certain author and how they have influenced the entire product using the data obtained from the source. Hence, you can enhance your team by either bringing in a new tester or upgrade the skill set of an existing tester.

Final Say

Analytics should aid software developers and designers in better testing software and creating optimal products for their customers. Better, more targeted activities will be driven by intelligent assessments and business insights gained from analytics. As a result, testing methodologies and test plans will be revised and re-engineered to provide analytics in automation with more opportunities to develop. In software testing, the age of analytics-driven automated testing is upon us.

To know more and proceed with an analytically-driven test automation approach, book a consultation with our testing experts at ImpactQA.



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