Effective fault localization and context-aware debugging for concurrent programs

Concurrent programs are difficult to debug because concurrency faults usually occur under specific inputs and thread interleavings. Fault localization techniques for sequential programs are often ineffective because the root causes of concurrency faults involve memory accesses across multiple threads rather than single statements. Previous research has proposed techniques to analyse passing and failing executions obtained from running a set of test cases for identifying faulty memory access patterns. However, stand-alone access patterns do not provide enough contextual information, such as the path leading to the failure, for developers to understand the bug. We present an approach, Coadec, to automatically generate interthread control flow paths that can link memory access patterns that occurred most frequently in the failing executions to better diagnose concurrency bugs. Coadec consists of two phases. In the first phase, we use feature selection techniques from machine learning to localize suspicious memory access patterns based on failing and passing executions. The patterns with maximum feature diversity information can point to the most suspicious pattern. We then apply a data mining technique and identify the memory access patterns that occurred most frequently in the failing executions. Finally, Coadec identifies faulty program paths by connecting both the frequent patterns and the suspicious pattern. We also evaluate the effectiveness of fault localization using test suites generated from different test adequacy criteria. We introduce and have evaluated Coadec on 10 real-world multithreaded Java applications. Results indicate that Coadec outperforms state-of-the-art approaches for localizing concurrency faults and that Coadec's context debugging can help developers understand concurrency fault by inspecting a small percentage of code.