Supporting software evolution using adaptive change propagation heuristics

When changing a source code entity (e.g., a function), developers must ensure that the change is propagated to related entities to avoid the introduction of bugs. Accurate change propagation is essential for the successful evolution of complex software systems. Techniques and tools are needed to support developers in propagating changes. Several heuristics have been proposed in the past for change propagation. Research shows that heuristics based on the change history of a project outperform heuristics based on the dependency graph. However, these heuristics being static are not the answer to the dynamic nature of software projects. These heuristics need to adapt to the dynamic nature of software projects and must adjust themselves for the peculiarities of each changed entity. In this paper we propose adaptive change propagation heuristics. These heuristics are metaheuristics that combine various previously researched heuristics to improve the overall performance (precision and recall) of change propagation heuristics. Through an empirical case study, using four large open source systems; GCC (a compiler), FreeBSD (an operating system), PostgreSQL (a database), and GCluster (a clustering framework), we demonstrate that our adaptive change propagation heuristics show a 57% statistically significant improvement over the top-performing static change propagation heuristics.