Abstract
Software quality assurance is one of the crucial factors for the success of software projects. Bug fixing has an essential role in software quality assurance, and bug localization (BL) is the first step of this process. BL is difficult and time-consuming since the developers should understand the flow, coding structure, and the logic of the program. Information retrieval-based bug localization (IRBL) uses the information of bug reports and source code to locate the section of code in which the bug occurs. It is difficult to apply other tools because of the diversity of software development languages, design patterns, and development standards. The aim of this study is to build an adaptive IRBL tool and make it usable by more companies. BugSTAiR solves the aforementioned problem by means of the adaptive attribute weighting (AAW) algorithm and is evaluated on four open-source projects which are well-known benchmark datasets on BL. One of them is BLIA which is the state of the art in bug localization area and another is BLUIR which is a well-known BL tool. According to the promising results of experiments, Top1 rank of BugSTAiR is 2% and MAP is 10% better than BLIA's results on AspectJ and it has localized 4.6% of all bugs in Top1 and its precision is 6.1% better than BLIA on SWT, respectively. On the other side, it is 20% better in the Top1 metric and 30% in precision than BLUIR.
Highlights
Many studies have been conducted to reduce maintenance costs in software development processes and to improve the quality of software, as evaluated considering different metrics [1, 2]
bug localization using integrated analysis (BLIA) has the highest performance on all datasets except Eclipse
The results of experiments show that BugSTAiR has promising performance on Java-based applications, so BugSTAiR outperforms any other bug localization (BL) tool
Summary
Many studies have been conducted to reduce maintenance costs in software development processes and to improve the quality of software, as evaluated considering different metrics [1, 2]. BugLocator performs on some high-scale open-source projects using text similarity between source files and bug reports It uses the information about fixed bugs to improve BL accuracy. Bug localization using information retrieval (BLUIR) uses structured information analysis of source code such as class names and method names It locates more bugs than BugLocator according to the experimental results on the same datasets. Youm et al [10] proposed bug localization using integrated analysis (BLIA) which considers stack traces, comments in bug reports, and change history of the source code for better accuracy Locus is another approach which uses source code and source code history in structured format [18].
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