HIPPODROME: Data Race Repair using Static Analysis Summaries
This addresses the challenge for programmers in maintaining bug-free concurrent code in large, evolving codebases, though it is incremental as it builds on existing static analysis methods.
The paper tackles the problem of fixing data races in concurrent Java programs by developing an automated repair technique that uses static analysis summaries to generate patches, achieving scalability and effectiveness in detecting and fixing more bugs than existing tools.
Implementing bug-free concurrent programs is a challenging task in modern software development. State-of-the-art static analyses find hundreds of concurrency bugs in production code, scaling to large codebases. Yet, fixing these bugs in constantly changing codebases represents a daunting effort for programmers, particularly because a fix in the concurrent code can introduce other bugs in a subtle way. In this work, we show how to harness compositional static analysis for concurrency bug detection, to enable a new Automated Program Repair (APR) technique for data races in large concurrent Java codebases. The key innovation of our work is an algorithm that translates procedure summaries inferred by the analysis tool for the purpose of bug reporting, into small local patches that fix concurrency bugs (without introducing new ones). This synergy makes it possible to extend the virtues of compositional static concurrency analysis to APR, making our approach effective (it can detect and fix many more bugs than existing tools for data race repair), scalable (it takes seconds to analyse and suggest fixes for sizeable codebases), and usable (generally, it does not require annotations from the users and can perform continuous automated repair). Our study conducted on popular open-source projects has confirmed that our tool automatically produces concurrency fixes similar to those proposed by the developers in the past.