Performance Evaluation of Data Race Detection Based on Thread Sharing Analysis With Different Granularities: An Empirical Study

Thread Sharing Analysis (TSA) plays an important role in concurrent program testing. Providing a TSA to a data race detector may speed up the runtime logging and improve the performance of data race detection. In this paper, we focus on the empirical study of the performance of data race detection based on TSA with different granularities. First, three granularities are considered, including object, field and “field + array element”. Then, an empirical study is conducted to evaluate the performance of data race detection based on three dynamic TSA approaches. The results show that data race detection based on the TSA with “field + array element” granularity outperforms those with object and field granularities.