Testing is a ripe field for applying AI because testing is fundamentally about inputs and expected outputs…… Testing combines lots of human and machine-generated data. Folks in testing often don’t have much exposure to AI, but that will change quickly, just like everyone else in the world is waking up to the power of AI.” – Jason Arbon, Author, and CEO of test.ai

We could say that automated software testing is essentially a quality control system that vets the operational aspects of a software product. The aim is to create a testing process that is rigorous and that operates through one or multiple test automation frameworks. Typically, upon completion, the tools report the results and compare outcomes with previous testing cycles. This is the age of Big Data and Analytics – it stands to reason that innovators have developed intelligent analytics solutions that offer insights designed to translate these test results into actionable information for future improvement. These solutions proactively identify problem areas in the testing process and indicate the way forward to achieve a high-quality software product. Let’s take a closer look at how analytics can help test automation.

Use of Analytics

In this context, analytics enables software developers to critically evaluate the performance of their test automation. They can track the various metrics and parameters involved in the creation of the test automation and the performance of the automated software testing exercise. Error logs embedded in the dashboard can spotlight the areas of improvement. Similarly, data about the number and the kind of functions that pass muster indicate the health of the software product that is being tested. The final status of the test results presents a perfect picture of the state of functionalities of the tested software. The graphical representations in the analytics dashboard portray a clear picture of testing outcomes that is easy to read and understand for everyone.

Predictive Analytics:

This aspect of analytics uses mathematical algorithms and machine learning technologies to forecast outcomes of software testing procedures. This technique uses current and past data to generate insights and locate potential points of failure in software testing outcomes. This enables the development and testing leaders to proactively address issues early in the lifecycle, and hence faster and easier. The use of predictive analytics also helps to detect delays and issues in software testing cycles. It also helps to monitor team productivity in testing cycles that involve human beings. Software developers can also run risk mitigation efforts when they use predictive analytics in testing procedures.

Benefits of Analytics in Testing:

Analytical reports draw on data that resides in multiple sources. This helps to present a more complete picture in real-time. The insights are clear and present, the actions to be taken are apparent, and the results can be tracked. The granular nature of the feedback generated by analytics should help software designers and testers to correct specific errors and the speed up slow processes.

The application of analytics should help software testing systems to overcome traditional or legacy limitations. The visual depiction of data in test performance and test history charts creates significant grounds to improve the testing procedures of the future. It is true that even today, automated software testing may fail for a variety of reasons, but the judicious application of analytics can increase the utility and the chances of success. In addition, interactive analytics-driven dashboards can offer enhanced monitoring and reporting capabilities for software testers and software developers. Further, analytics helps to expand the productivity of complex software testing tools while boosting the productivity of the testing team. This can help to release higher-quality products faster and more often.

The combination of test automation and advanced analytics will enable software development and testing managers to spend more time on strategic activities that drive greater business value over a longer term.

The Future of Automation in Testing:

Enterprises today are driving a relentless focus on quality. Current and future products are undergoing design changes that will make them even more intuitive and easy to use. The user interfaces will be the most critical aspect and they must be tested for reliable operation at all times. The deployment of analytics should help software developers and designers to better test software and create perfect products for clients. Intelligent observations and business insights derived from analytics will drive better, more targeted actions. Therefore, testing strategies and test plans will be refined and re-engineered to create greater scope for analytics in automation. It’s all set to be the and analytics-driven automated age in software testing – are your plans ready?