关键词: 数据竞争, 并发缺陷, Web服务组合, 业务过程管理, 逻辑时钟

Abstract: Traditional dynamic data race detection methods model activity causal relationship in a less precise way.Furthermore,mutual exclusion relationship is causal constraints that lead to various misreports or underreporting .To address this problem,a finer classification of causal relationships among Business Process Execution Language (BPEL) activities was proposed.A new logic clock-based method for finer identification of causal relationships was developed.Meanwhile,an accurate processing of mutually exclusive relationships among activities in BPEL programs was proposed by combining vector clocks and global mutex locks together.As a result,a more refined method for dynamic detection and prediction of data races in BPEL programs was proposed,which was able to ensure both a lower rate of data race false positives and a lower rate of data race negatives to a certain extent,but at the expense of more space and time.The advantages of the proposed method were verified in conjunction with a BPEL process example.

Key words: data race, concurrency defect, Web service composition, business process management, logic clock