Evolutionary Algorithm for Bug Localization in the Reconfigurations of Models at Runtime

Systems with models at runtime are becoming increasingly complex, and this is also accompanied by more software bugs. In this paper, we focus on bugs appearing as the result of dynamic reconfigurations of the system due to context changes. We materialize our approach for bug localization in reconfigurations as an evolutionary algorithm. We guide the evolutionary algorithm with a fitness function that measures the similarity to the description of the bug report. The result is a ranked list of reconfiguration sequences, which is intended to identify the reconfiguration rules that are relevant to the bug. We evaluated our approach in BSH and CAF, two real-world industrial case studies, measuring the results in terms of recall, precision, F-measure and Matthews Correlation Coefficient (MCC). In our evaluation, we compare our approach with two other approaches: a baseline that is the one used by our industrial partners for bug localization and a random search as sanity check. Our study shows that our approach, which takes advantage of the reconfigurations of models at runtime, outperforms the other two approaches. We also performed a statistical analysis to provide evidence of the significance of the results.