Gheyi, On relating technical, social factors, and the introduction of bugs, 27th IEEE Int. Ho, An analysis of software bug reports using machine learning techniques, SN Comput. González-Barahona, How bugs are born: a model to identify how bugs are introduced in software components, Empiri. Liu, Topic modeling using topics from many domains, lifelong learning and big data, in Proc. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2016, pp. Hu, Exploring topic models in software engineering data analysis: A survey, 17th IEEE/ACIS Int. Yang, FRLink: Improving the recovery of missing issue-commit links by revisiting file relevance, Inform. Software Analysis, Evolution, and Reengineering, 2016, pp. Murphy, An empirical study on recommendations of similar bugs, IEEE 23rd Int. Software Maintenance and Evolution, 2017, pp. Dubash, Towards accurate duplicate bug retrieval using deep learning techniques, IEEE Int. Greiner, Detecting duplicate bug reports with software engineering domain knowledge, J. Bugayenko, Discovering bugs, or ensuring success? Commun. Li, Construct bug knowledge graph for bug resolution: Poster, in Proc. Mahmoud, Just enough semantics: An information theoretic approach for IR-based software bug localization, Inform. Cai, How security bugs are fixed and what can be improved: an empirical study with Mozilla, Sci. Software Engineering: Companion Proceeedings, 2018, pp. Le Goues, BugZoo: a platform for studying software bugs, in Proc. Foundations of Software Engineering, 2018, pp. ACM Joint Meeting on European Software Engineering Conf. Zhou, Intelligent bug fixing with software bug knowledge graph, in Proc. Li, Enhancing developer recommendation with supplementary information via mining historical commits, J. Liao, Effectiveness of exploring historical commits for developer recommendation: an empirical study, Front. Lu, Bug localization for version issues with defect patterns, IEEE Access 7 ( 2019) 18811–18820. Poshyvanyk, RCLinker: automated linking of issue reports and commits leveraging rich contextual information, in Proc. Li, An empirical study on real bugs for machine learning programs, 24th Asia-Pacific Software Engineering Conf., 2017, pp. Sun, Recommending frequently encountered bugs, in Proc. Guo, Recognizing software bug-specific named entity in software bug repository, in Proc. The experiment results show that our approach is effective and efficient to help developers search relevant bugs for reference by constructing the bug knowledge as a service. Finally, the experiment with the bug reports from and the corresponding commits from Github was conducted. We can automatically update the bug knowledge graph with the LTM topic model (a lifelong topic model). In addition, as the amount of bug related information continuously increase, it is time-consuming to update the data. To deal with these problems, this paper proposes an approach to deal with the bug and commit information with the topic model, and construct bug knowledge graph as a service to assist in bug search. What’s more, many searching results are not accurate. When developers search a bug issue, they can only get the information of bug reports or commits, which are loose and difficult for developers to refer. However, the links between bug reports and commits in version control systems are often missed, and the information in bug repository and commit repository can provide is simple. When encountering bug issues, developers tend to search the bug repository and commit repository for references.
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