AI-Driven Software Defect Intelligence in the Age of DevOps
With the rise of software systems ranging from personal assistance to the nation’s facilities, software defects become more critical concerns as they can cost millions of dollar as well as impact human lives. Yet, at the breakneck pace of rapid software development settings (like DevOps paradigm), the Quality Assurance (QA) practices (e.g., code review and software testing) nowadays are still time-consuming. This project aims to develop an end-to-end AI platform that leverages advanced machine intelligence techniques (e.g., Deep Learning, Statistics, ML, Optimization) in order to (1) understand the nature of poor code changes; (2) predict the most risky changes that will introduce defects in the future; (3) highlight hotspot areas; (4) explain and visualise the characteristics of risky changes; (5) suggest potential patches for defect fixing; and (6) integrate such platform into a real-world practice of rapid development cycles like GitHub ecosystem. Finally, this project will be deployed and evaluated in ultra-large-scale software systems like Google, OpenStack, Eclipse, Mozilla, Linux software systems. The outcome of this project is expected to help early detect and remove critical software defects, and help project managers establish an effective quality improvement policy.
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