Available online: https://edupediapublications.org/journals/index.php/IJR/ P a g e | 1919 Mapping Bug Reports To Relevant Files:A Ranking Model, A FineGrained Benchmark,And Feature Evaluation

Abstract

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files with respect to how likely they are tocontain the cause of the bug would enable developers to narrow down their search and improve productivity. This paper introduces an adaptive ranking approach that leverages project knowledge through functional decomposition of source code, API descriptions oflibrary components, the bug-fixing history, the code change history, and the file dependency graph. Given a bug report, the rankingscore of each source file is computed as a weighted combination of an array of features, where the weights are trained automatically onpreviously solved bug reports using a learning-to-rank technique. We evaluate the ranking system on six large scale open source Javaprojects, using the before-fix version of the project for every bug report. The experimental results show that the learning-torankapproach outperforms three recent state-of-the-art methods. In particular, our method makes correct recommendations within the top10 ranked source files for over 70 percent of the bug reports in the Eclipse Platform and Tomcat projects.