Can Commit Change History Reveal Potential Fault Prone Classes? A Study on GitHub Repositories

Various studies had successfully utilized graph theory analysis as a way to gain a high-level abstraction view of the software systems, such as constructing the call graph to visualize the dependencies among software components. The level of granularity and information shown by the graph usually depends on the input such as variable, method, class, package, or combination of multiple levels. However, there are very limited studies that investigated how software evolution and change history can be used as a basis to model software-based complex network. It is a common understanding that stable and well-designed source code will have less update throughout a software development lifecycle. It is only those code that were badly design tend to get updated due to broken dependencies, high coupling, or dependencies with other classes. This paper put forward an approach to model a commit change-based weighted complex network based on historical software change and evolution data captured from GitHub repositories with the aim to identify potential fault prone classes. Four well-established graph centrality metrics were used as a proxy metric to discover fault prone classes. Experiments on ten open-source projects discovered that when all centrality metrics are used together, it can yield reasonably good precision when compared against the ground truth.