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Organizations leveraging traditional software testing tools often fail to scale to the needs of today’s digital demands. That’s the top-level finding of a new report published by EMA Research and Applitools, which examined the impact of AI and automated software testing on large enterprises. Respondents cited escalating quality control costs and release velocities as the top factors hindering engineering and DevOps efforts, as well as the increasing number of target devices, operating systems, and app programming languages.
“Business’ ability to accelerate the delivery of customer value through software innovation, at lower cost, has become critical for achieving competitive advantages,” Enterprise Management Associates managing research director Torsten Volk said in a statement.
The report shows that there’s been nearly a 100% uptick in the number of test automation-related questions posted to Stack Overflow, a popular Q&A website for programmers, over the past year. The accelerated adoption of apps that reside on cloud services — up 225% since 2015 — is further compounding the complexity of software delivery, according to EMA Research and Applitools.
The results of a recent Gatepoint Research survey similarly suggest that software delivery problems are becoming common in the enterprise. A whopping 77% of the respondents said that they experience setbacks in releasing new software, with 34% admitting that fixing bugs takes anywhere from days to months.
The growing number of monthly code releases, multiplied by the set of daily tasks engineers have to complete, is resulting in an “exponential” increase in required testing, EMA Research and Applitools found. Gatepoint identified other software deployment challenges, including developing and testing software in an environment that’s different from the production environment, an over-reliance on manual processes, and complex app dependencies and workflows.
The EMA Research and Applitools paper outlines five areas where AI and machine learning could solve major blockers in software delivery: test creation, self-healing, visual inspection, coverage detection, and anomaly detection. Taken together, these technologies have the potential to streamline and automate parts of the software testing workflow while enhancing productivity, according to Volk.
Test creation automates the discovery of new and changed test requirements by analyzing changes in apps and documentation. Self-healing fixes broken test workflows while visual inspection trains models to audit apps through the eyes of end-users. Meanwhile, coverage detection and anomaly detection identify the different paths that end-users can take through apps and report gaps in code coverage or anomalous behavior.
According to one report, the majority of companies see a return on their test automation investment immediately or within the first six months. Only a small percentage — 9% — report never getting a return on investment.
“AI-based test automation technologies can deliver real return on investment today and come with the potential of addressing, and ultimately eliminating, today’s critical automation bottlenecks that stifle modern software delivery,” Volk said.
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