Predicting Faults from Direct Semantic Interference: An Evaluative Experiment

Parallel developments are becoming increasingly prevalent in the building and evolution of large-scale software systems. Our previous studies of a large industrial project showed that there was a linear correlation between the degree of parallelism and the likelihood of defects in the changes. To further study the relationship between parallel changes and faults, we have designed and implemented an algorithm to detect “direct” semantic interference between parallel changes. To evaluate the analyzer’s effectiveness in fault prediction, we designed an experiment 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 analyzer in predicting faults. Our contributions in this evaluative empirical study are twofold.. First, we evaluate the semantic interference analyzer and show that it is effective in predicting faults (based on “direct” semantic interference detection) in changes made within a short time period. Second, the design of our experiment is itself a significant contribution and exemplifies how to mine software repositories rather than use artificial cases for rigorous experimental evaluations.