Improving Enterprise Scale Test Automation with ML-Based Predictive Analytics
AI and machine learning continue to make considerable progress in advancing state of the art computing. Recently, the focus is on generative AI systems like ChatGPT. However, these models encompass a fundamental aspect of ML, the ability to observe and analyze historical data and make predictions. Automation is a core component of any testing strategy, but is especially important in enterprise projects with hundreds of thousands of tests. Collecting, coalescing, and analyzing the results of large-scale test automation projects across multiple levels and types of testing is challenging. However, incorporating AI and ML into the testing process, including leveraging it to enhance test reporting and analytics, is an effective way to improve large scale test automation. This paper describes some of the key challenges faced in large scale test automation projects and provides a summary of how AI and ML are being applied in practice to overcome those challenges. Emphasis is placed on the stability of executing automated tests at scale, and providing meaningful, actionable insights from the results. The findings and experiences from enterprise case studies are summarized, including one that leverages an open-source, AI-powered test automation dashboard.
Dmitriy Gumeniuk is the Head of Testing Products and Senior Delivery Manager at EPAM Systems, a leading global provider of digital platform engineering and software development services. Dmitriy led development of solution accelerators at Test Competency Center, focusing of Machine Learning and Neural Networks usage in test automation such as Healenium, Drill4J, TDSpora.ai and ReportPortal. With 16 years of experience in software development, he has provided a technical leadership for java development teams and can advance test automation at any scale. Dmitriy contributes to DevTestOps local communities by leading local meetups, actively speaking at events in CIS and eastern Europe region, and organizing DelEx Conference, which aims to inspire DevTestOps practice.