Inferring Performance Bug Patterns from Developer Commits Performance bugs, i.e., program source code that is unnecessarily inefficient, have received significant attention by the research community in recent years. A number of empirical studies have investigated how these bugs differ from ordinary bugs that cause functional deviations and several approaches to aid their detection, localization, and removal have been proposed. Many of these approaches focus on certain subclasses of performance bugs, e.g., those resulting from redundant computations or unnecessary synchronization, and the evaluation of their effectiveness is usually limited to a small number of known instances of these bugs. To provide researchers working on performance bug detection and localization techniques with a larger corpus of performance bugs to evaluate against, we conduct a study of more than 700 performance bug fixing commits across 13 popular open source projects written in C and C and investigate the relative frequency of bug types as well as their complexity. Our results show that many of these fixes follow a small set of bug patterns, that they are contributed by experienced developers, and that the number of lines needed to fix performance bugs is highly project dependent.

How to Explain a Patch: An Empirical Study of Patch Explanations in Open Source Projects AbstractBugs are inevitable in software development and maintenance processes. Recently a lot of research efforts have been devoted to automatic program repair, aiming to reduce the efforts of debugging. However, since it is difficult to ensure that the generated patches meet all quality requirements such as correctness, developers still need to review the patch. In addition, current techniques produce only patches without explanation, making it difficult for the developers to understand the patch. Therefore, we believe a more desirable approach should generate not only the patch but also an explanation of the patch. To generate a patch explanation, it is important to first understand how patches were explained. In this paper, we explored how developers explain their patches by manually analyzing 300 merged bugfixing pull requests from six projects on GitHub. Our contribution is twofold. First, we build a patch explanation model, which summarizes the elements in a patch explanation, and corresponding expressive forms. Second, we conducted a quantitative analysis to understand the distributions of elements, and the correlation between elements and their expressive forms.