On The Relationship Between The Vocabulary Of Bug Reports And Source Code

The use of text retrieval techniques on concept location and bug localization yields remarkable benefits. The artifacts found in source code and bug reports contain important information related to the bug localization process. When locating the bugs, it is a programmer\u27s task to formulate effective queries such that most of the predicted terms in the query appear in the relevant defect code, but not in most of the non-relevant source files. These queries are built based on the textual content found in the bug reports, especially the bug title and the description. A large body of research uses bug descriptions to evaluate bug localization techniques using text retrieval. All these studies are conducted under the implicit assumption that the bug description and the relevant source code files share important terms. This paper presents an empirical study that explores this conjecture. We found that bug reports share more terms with the patched classes than with the other classes in the software system. Moreover, the study revealed that the class names are more likely to share terms with the bug descriptions than other code locations. We also found that more verbose parts of the source code, such as, comments share more words. Furthermore, we discovered that the shared terms may be better predictors for bug localization than some other text retrieval techniques, such as, LSI

Locating bugs without looking back

Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history

User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table