On the Effectiveness of Information Retrieval Based Bug Localization for C Programs

International audienceLocalizing bugs is important, difficult, and expensive, especially for large software projects. To address this problem, information retrieval (IR) based bug localization has increasingly been used to suggest potential buggy files given a bug report. To date, researchers have proposed a number of IR techniques for bug localization and empirically evaluated them to understand their effectiveness. However, virtually all of the evaluations have been limited to the projects written in object-oriented programming languages, particularly Java. Therefore, the effectiveness of these techniques for other widely-used languages such as C is still unknown. In this paper, we create a benchmark dataset consisting of more than 7,500 bug reports from five popular C projects and rigorously evaluate our recently introduced IR-based bug localization tool using this dataset. Our results indicate that although the IR-relevant properties of C and Java programs are different, IR-based bug localization in C software at the file level is overall as effective as in Java software. However, we also find that the recent advance of using program structure information in performing bug localization gives less of a benefit for C software than for Java software

Recommending Relevant Classes for Bug Reports Using Multi-Objective Search

Developers may follow a tedious process to find the cause of a bug based on code reviews and reproducing the abnormal behavior. In this thesis, we propose an automated approach for finding and ranking potential classes with the respect to the probability of containing a bug based on a bug report description. Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a multi-objective optimization algorithm. The relevance of the recommended classes (solution) is estimated based on the use of the history of changes and bug-fixing, and the lexical similarity between the bug report description and the API documentation. We evaluated our system on 6 open source Java projects including more than 22,000 bug reports, using the version of the project before fixing the bug of many bug reports. The experimental results show that the search-based approach significantly outperforms three state-of-the-art methods in recommending relevant files for bug reports. In particular, our multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 87% of the bug reports.Master of ScienceSoftware Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136064/1/Recommending Relevant Classes for Bug Reports Using Multi-Objective Search.pdfDescription of Recommending Relevant Classes for Bug Reports Using Multi-Objective Search.pdf : Master of Science Thesi

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin