Machine Learning-Powered Automated Software Bug Detection

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Today, software is integrated into every part of our society. Recent research shows that software faults could cost the global economy over 300 billion dollars annually. Building reliable and secure software has become an increasingly critical challenge for software developers. Thus the techniques for helping software developers detect software faults and improve software quality and reliability will be even more important than ever before. To help developers detect faults, over the last years, many static bug detection approaches have been proposed and some have been adopted in industry. A typical static bug detector leverages programming rules or patterns to detect specific types of bugs. Despite the overall success of static bug detection, it suffers from the following major challenging issues. First, current static bug detectors report a large number of false positives, i.e., reported bugs that are not actually bugs. Recent studies show that 30-90% of reported bugs by static bug detection tools are false positives. Second, current bug detectors miss detecting around 95% of real-world bugs, which suggests more bug patterns should be learned to detect bugs. Third, although a large number of bugs have been detected by current bug detection tools, developers still have to manually fix these bugs, which is time-consuming and requires nontrivial expertise. The goal of this proposal is to address the above-mentioned challenges when using static bug detection tools to help developers find bugs and improve software quality. This proposal has three research objectives (ROs). RO1: To help remove the false positives generated by static bug detectors, my students and I will leverage deep learning (DL) techniques to learn more powerful classification models that could distinguish the semantic difference between false positives and true bugs, and the model will be further integrated into existing static bug detection tools to help filter out false positives. RO2: To help find new bug patterns, my students and I will focus on history bugs that cannot be detected by existing bug detection tools and create machine learning based techniques to capture potential bug patterns. RO3: To help automatically fix bugs detected by existing static bug detectors, my students and I will propose DL based approaches to automatically repair bugs detected by static bug detectors by learning potential fixes from pattern information, historical fixes, and context information of the bugs. The outcome of this research will provide an actionable solution to assist developers with modern software bug detection tools. The proposed techniques will significantly improve software quality and reduce software debugging and development costs among Canadian companies, e.g., Shopify and RIM. The proposed research will also train five highly qualified personnel (HQP) and allow them to contribute to state-of-the-art software engineering research and practice.