Augmenting Bug Localization with Part-of-Speech and Invocation

Abstract

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous 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, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of our approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e., ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

Software fault localisation: a systematic mapping study

“…Unlike spectrum‐based and statistical‐based techniques, IR‐based techniques do not require program execution information (program spectra and test results) to locate bugs. Bugs are located sorely based on initial bug reports [43, 44]. Mutation‐based techniques analyse diverse program behaviour using mutants (program versions generated by applying simple syntactic code change).…”

Code Complexity and Version History for Enhancing Hybrid Bug Localization

“…The results show the performance of the proposed approach that applied on three open-source projects (e.g. AspectJ, SWT, and Zxing ) can locate appropriate buggy source code files up to 52 % by recommending one source code file and up to 78 % by recommending top ten source code files compared to state-ofthe-art approaches [7,10,11,13,14].…”

“…Zhou et al [14] presented an approach that leverages the feature of part-of-speech in bug reports and the relationship between source code files to enhance the performance of bug localization. The results show over six open source projects (e.g.…”

“…Furthermore, English stop words were removed such as punctuation marks, numbers, and keywords of programming language. Stemming and stop word removal reduce the BugLocator [2] BLUiR [8] Amalgam [6] BRTracer [11] Rahman et al [12] AmaLgam+ [13] Zhou et al [14] BLIA v1.5 [16] Gharibi et al [17] number of tokens and remove the noise. Finally, parts of speech (POS) tagger was applied for classifying the tokens into a noun, verb, etc.…”

Bug Localization for Version Issues With Defect Patterns

“…It utilizes the information extracted from the build logs, and a heuristic rule-based filtering component to automatically produce a ranked list of files [22]. Zhou et al [23] leveraged the part-of-speech features of bug reports and the invocation relationship among source files to locate bugs. Sisman et al [24] took Markov Random Field (MRF) that is based on retrieval framework into account for improving the retrieval quality of the most relevant source files.…”