Detecting Semantic Interference in Parallel Changes: An Exploratory Case Study

Parallel changes are becoming increasingly prevalent in the development of large scale software system. To further study the relationship between parallel changes and faults, we have designed and implemented an algorithm to detect semantic interference between parallel changes. To evaluate the effectiveness and efficiency of this analyzer, we designed an exploratory case study in the context of an industrial project. We first mine the change and version management repositories to find sample versions sets of different degrees of parallelism. We investigate the interference between the versions with our analyzer. We then mine the change and version repositories to find out what faults were discovered subsequent to the analyzed interfering versions. We use the match rate between semantic interference and faults to evaluate the effectiveness of the semantic interference detection tool. We also evaluate its efficiency by the lapse for finding (an average of 150 days) and fixing the faults associated with those samples. The case study shows that the analyzer is most effective in detecting non-pointer variable interference in adaptive changes with a high degree of parallelism. Further, the analyzer is both efficient (averaging less than two minutes) and scalable (requiring only the local context).