@conference {Wang:2008:ADD:1368088.1368151,
	title = {An approach to detecting duplicate bug reports using natural language and execution information},
	booktitle = {Proceedings of the 30th international conference on Software engineering},
	series = {ICSE {\textquoteright}08},
	year = {2008},
	pages = {461{\textendash}470},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {An open source project typically maintains an open bug repository so that bug reports from all over the world can be gathered. When a new bug report is submitted to the repository, a person, called a triager, examines whether it is a duplicate of an existing bug report. If it is, the triager marks it as DUPLICATE and the bug report is removed from consideration for further work. In the literature, there are approaches exploiting only natural language information to detect duplicate bug reports. In this paper we present a new approach that further involves execution information. In our approach, when a new bug report arrives, its natural language information and execution information are compared with those of the existing bug reports. Then, a small number of existing bug reports are suggested to the triager as the most similar bug reports to the new bug report. Finally, the triager examines the suggested bug reports to determine whether the new bug report duplicates an existing bug report. We calibrated our approach on a subset of the Eclipse bug repository and evaluated our approach on a subset of the Firefox bug repository. The experimental results show that our approach can detect 67\%-93\% of duplicate bug reports in the Firefox bug repository, compared to 43\%-72\% using natural language information alone.},
	keywords = {bug report, duplicate bug report, execution information, information retrieval, natural language},
	isbn = {978-1-60558-079-1},
	doi = {10.1145/1368088.1368151},
	url = {http://doi.acm.org/10.1145/1368088.1368151},
	author = {Wang, Xiaoyin and Zhang, Lu and Xie, Tao and Anvik, John and Sun, Jiasu}
}
@conference {992,
	title = {Determining Implementation Expertise from Bug Reports},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {2 - 2},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {As developers work on a software product they accumulate expertise, including expertise about the code base of the software product. We call this type of expertise "implementation expertise". Knowing the set of developers who have implementation expertise for a software product has many important uses. This paper presents an empirical evaluation of two approaches to determining implementation expertise from the data in source and bug repositories. The expertise sets created by the approaches are compared to those provided by experts and evaluated using the measures of precision and recall. We found that both approaches are good at finding all of the appropriate developers, although they vary in how many false positives are returned.},
	keywords = {bug reports, developers, eclipse, expertise, repository, scm, source code},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.7},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300002.pdf},
	author = {Anvik, John and Murphy, Gail C.}
}
@conference {Anvik:2006:FTB:1134285.1134336,
	title = {Who should fix this bug?},
	booktitle = {Proceedings of the 28th international conference on Software engineering},
	series = {ICSE {\textquoteright}06},
	year = {2006},
	pages = {361{\textendash}370},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Open source development projects typically support an open bug repository to which both developers and users can report bugs. The reports that appear in this repository must be triaged to determine if the report is one which requires attention and if it is, which developer will be assigned the responsibility of resolving the report. Large open source developments are burdened by the rate at which new bug reports appear in the bug repository. In this paper, we present a semi-automated approach intended to ease one part of this process, the assignment of reports to a developer. Our approach applies a machine learning algorithm to the open bug repository to learn the kinds of reports each developer resolves. When a new report arrives, the classifier produced by the machine learning technique suggests a small number of developers suitable to resolve the report. With this approach, we have reached precision levels of 57\% and 64\% on the Eclipse and Firefox development projects respectively. We have also applied our approach to the gcc open source development with less positive results. We describe the conditions under which the approach is applicable and also report on the lessons we learned about applying machine learning to repositories used in open source development.},
	keywords = {bug fixing, bug report, bug report assignment, bug triage, eclipse, Firefox, gcc, issue tracking, machine learning, problem tracking},
	isbn = {1-59593-375-1},
	doi = {10.1145/1134285.1134336},
	url = {http://doi.acm.org/10.1145/1134285.1134336},
	author = {Anvik, John and Hiew, Lyndon and Murphy, Gail C.}
}
