Mining repair model for exception-related bug

Abstract

It has long been a hot research topic to detect and to repair bugs automatically. As a common practice, researchers propose approaches for specific bugs, and their approaches typically are limited in handling the variety among bugs. Recently, researchers start to explore automatic program repair. With predefined repair operators and test cases, test-based repair approaches use search algorithms to generate patches for a bug, until a patch passes all the test cases. To improve the effectiveness to generate patches, Martinez and Monperrus (2013b) proposed an approach that mines repair models from past fixes. Although their approach produces positive results, we argue that it can be feasible to further improve their approach, if we mine repair models for bug categories, instead of all bugs. However, the benefits are still unclear, since existing benchmarks do not classify bugs into categories and existing approaches cannot mine repair models for bug categories. In this paper, we implement a tool, called ExFi, that classifies bugs into categories based on their related exceptions. With its support, we construct a benchmark, in which bug categories are marked. Furthermore, we propose an approach, called MiMo, that mines a repair model for each exception. We compared the general repair model with our mined repair models. Our results show that our mined models are all significantly different from the general model. Outside of the projects where our models are mined, we selected 59 real bugs. For each bug, we used our models and the general model to generate correct repair shapes for these bugs. The results show that for 43 out of 59 bugs, our models found faster a correct shape than the general repair model (Martinez and Monperrus, 2013b), and for 5 bugs, our models were able to find correct shapes that were not found by the compared model.