Effective and Precise Dynamic Detection of Hidden Races for Java Programs

Happens-before relation is widely used to detect data races dynam-ically. However, it could easily hide many data races as it is inter-leaving sensitive. Existing techniques based on randomized sched-uling are ineffective on detecting these hidden races. In this paper, we propose DrFinder, an effective and precise dynamic technique to detect hidden races. Given an execution, DrFinder firstly ana-lyzes the lock acquisitions in it and collects a set of "may-trigger" relations. Each may-trigger relation consists of a method and a type of a Java object. It indicates that, during execution, the method may directly or indirectly acquire a lock of the type. In the subsequent executions of the same program, DrFinder actively schedules the execution according to the set of collected may-trigger relations. It aims to reverse the set of happens-before relation that may exist in the previous executions so as to expose those hidden races. To ef-fectively detect hidden races in each execution, DrFinder also col-lects a new set of may-trigger relation during its scheduling, which is used in its next scheduling. Our experiment on a suite of real-world Java multithreaded programs shows that DrFinder is effec-tive to detect 89 new data races in 10 runs. Many of these races could not be detected by existing techniques (i.e., FastTrack, Con-Test, and PCT) even in 100 runs