IoT & Embedded Technology Blog

Developers Need More AI/ML in Software Testing, Who Will Answer the Call

by Rob Shapiro | 09/11/2020

The evolution of device functionality continues to expand performance requirements of software. In turn, software becomes more complex year after year. Meanwhile, the surge in organizations adopting Agile and DevOps redefines their approach to software development with rapid release and testing cycles for faster time to market competence. With pressures mounting for developers to produce more software in less time, there is a strong need for enhanced automated testing in order to keep pace.

The incorporation of artificial intelligence and machine learning in automated testing will help meet this heightened demand going forward. Several test tools suppliers, such as MathWorks, Micro Focus and Parasoft, introduced AI/ML enhanced testing solutions over the past couple years to aid in test creation and optimize test execution. However, these initiatives just scratch the surface of AI/ML functions for automated testing. In the highly competitive software and security testing market more vendors will soon follow, producing their own innovative testing solutions with AI/ML capabilities across various testing types. Automated testing tools vendors will need to invest in research of AI/ML in software testing, and find innovative ways to incorporate machine learning in their solutions in order to stay competitive going forward.

Feedback from software developers will be crucial information to guide vendors’ exploration of the benefits AI/ML brings to automated software testing. Developers utilizing software testing are already much more likely to use AI/ML in their future projects, and serve as the perfect population to leverage this feedback. Aside from testing tools’ utility addressing these more complex projects, the budding integration of AI/ML into the testing tools themselves will prove an increasingly valuable feature for development organizations to identify and prioritize code issues.

Figure 1: Use of Machine Learning/Neural Networks on Current and Future Projects, by Testing Usage

In this race for high quality AI/ML enhanced automated testing solutions, which vendors lead the charge? Which markets present the most opportunity for vendors providing these testing solutions? How many software developers leverage machine learning in their current projects? How does the utilization of AI/ML differ between those developers employing software testing against those who do not? VDC’s report “The Global Market for Automated Software and Security Testing Tools” tackles these questions surrounding artificial intelligence in the automated testing tools market as well as various geographic, industry-specific, tool-type, and end-user trends.

To learn more, view our recently published Automated Software and Security Testing Tools report.