Towards a novel approach for defect localization based on part-of-speech and invocation

Given a corpus of bug reports, software developers must read various descriptive sentences in order to identify corresponding buggy source files which potentially result in the defects. This process itself represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, many methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source file localization approach, leveraging both a part-of-speech based weighting strategy and the invocation relationship among source files. We also integrate an adaptive technique to strengthen the optimization of the performance. The adaptive technique consists of two modules. One is to maximize the accuracy of the first recommended file, and the other aims at improving the accuracy of the fixed defect file list. We evaluate our approach on three large-scale open source projects, i.e., ASpectJ, Eclipse, and SWT. Compared with the baseline work, our approach can improve 17.13%, 6.29% and 3.15% on top 1, top 5 and top 10 respectively for ASpectJ, 6.40%, 4.94% and 4.39% on top 1, top 5 and top 10 respectively for Eclipse, and 15.31%, 8.16% and 5.10% on top 1, top 5 and top 10 respectively for SWT.