Persuasive prediction of concurrency access anomalies

Predictive analysis is a powerful technique that exposes concurrency bugs in un-exercised program executions. However, current predictive analysis approaches lack the persuasiveness property as they offer little assistance in helping programmers fully understand the execution history that triggers the predicted bugs. We present a persuasive bug prediction technique as well as a prototype tool, PECAN, for detecting general access anomalies (AAs) in concurrent programs. The main characteristic of PECAN is that, in addition to predict AAs in a more general way, it generates "bug hatching clips" that deterministically instruct the input program to exercise the predicted AAs. The key ingredient of PECAN is an efficient offline schedule generation algorithm, with proof of the soundness, that guarantees to generate a feasible schedule for every real AA in programs that use locks in a nested way. We evaluate PECAN using twenty-two multi-threaded subjects including six large concurrent systems, and our experiments demonstrate that PECAN is able to effectively predict and deterministically expose real AAs. Several serious and previously unknown bugs in large open source concurrent systems were also revealed in our experiments.