@conference {961,
	title = {Do stack traces help developers fix bugs?},
	booktitle = {2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)},
	year = {2010},
	pages = {118 - 121},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Cape Town, South Africa},
	abstract = {A widely shared belief in the software engineering community is that stack traces are much sought after by developers to support them in debugging. But limited empirical evidence is available to confirm the value of stack traces to developers. In this paper, we seek to provide such evidence by conducting an empirical study on the usage of stack traces by developers from the ECLIPSE project. Our results provide strong evidence to this effect and also throws light on some of the patterns in bug fixing using stack traces. We expect the findings of our study to further emphasize the importance of adding stack traces to bug reports and that in the future, software vendors will provide more support in their products to help general users make such information available when filing bug reports.},
	keywords = {bug fixing, bug report, debugging, eclipse, stack trace},
	isbn = {978-1-4244-6802-7},
	doi = {10.1109/MSR.2010.5463280},
	attachments = {https://flosshub.org/sites/flosshub.org/files/118-10-msr.pdf},
	author = {Schroter, Adrian and Schr{\"o}ter, Adrian and Bettenburg, Nicolas and Premraj, Rahul}
}
@article {10.1109/TSE.2010.63,
	title = {What Makes a Good Bug Report?},
	journal = {IEEE Transactions on Software Engineering},
	volume = {36},
	year = {2010},
	note = {"o find out which matter most, we asked 872 devel-
opers from the APACHE, ECLIPSE, and MOZILLA projects to:
1. Complete a survey on important information in bug reports and the problems they faced with them. We received a total of 156 responses to our survey (Section 2 and 3).
2. Rate the quality of bug reports from very poor to very good on a five-point Likert scale [22]. We received a total of 1,186 votes for 289 randomly selected bug reports (Section 4).
In addition, we asked 1,354 reporters1 from the same projects to complete a similar survey, out of which 310 responded. "},
	pages = {618-643},
	publisher = {IEEE Computer Society},
	address = {Los Alamitos, CA, USA},
	abstract = {In software development, bug reports provide crucial information to developers. However, these reports widely differ in their quality. We conducted a survey among developers and users of APACHE, ECLIPSE, and MOZILLA to find out what makes a good bug report.
The analysis of the 466 responses revealed an information mis- match between what developers need and what users supply. Most developers consider steps to reproduce, stack traces, and test cases as helpful, which are at the same time most difficult to provide for users. Such insight is helpful to design new bug tracking tools that guide users at collecting and providing more helpful information.
Our CUEZILLA prototype is such a tool and measures the quality of new bug reports; it also recommends which elements should be added to improve the quality. We trained CUEZILLA on a sample of 289 bug reports, rated by developers as part of the survey. In our experiments, CUEZILLA was able to predict the quality of 31{\textendash}48\% of bug reports accurately.},
	keywords = {bug report, Survey},
	issn = {0098-5589},
	doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2010.63},
	url = {http://dl.acm.org/citation.cfm?id=1453146},
	attachments = {https://flosshub.org/sites/flosshub.org/files/bettenburg-fse-2008.pdf},
	author = {Zimmermann, Thomas and Premraj, Rahul and Bettenburg, Nicolas and Sascha Just and Schroter, Adrian and Weiss, Cathrin}
}
@conference {972,
	title = {Extracting structural information from bug reports},
	booktitle = {Proceedings of the 2008 international workshop on Mining software repositories  - MSR {\textquoteright}08},
	year = {2008},
	month = {05/2008},
	pages = {27-30},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {New York, New York, USA},
	abstract = {In software engineering experiments, the description of bug reports is typically treated as natural language text, although it often contains stack traces, source code, and patches. Neglecting such structural elements is a loss of valuable information; structure usually leads to a better performance of machine learning approaches. In this paper, we present a tool called infoZilla that detects structural elements from bug reports with near perfect accuracy and allows us to extract them. We anticipate that infoZilla can be used to leverage data from bug reports at a different granularity level that can facilitate interesting research in the future.},
	keywords = {bug reports, eclipse, enumerations, infozilla, natural language, patches, source code, stack trace},
	isbn = {9781605580241},
	doi = {10.1145/1370750.1370757},
	attachments = {https://flosshub.org/sites/flosshub.org/files/p27-bettenburg.pdf},
	author = {Premraj, Rahul and Zimmermann, Thomas and Kim, Sunghun and Bettenburg, Nicolas}
}
@conference {991,
	title = {How Long Will It Take to Fix This Bug?},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {1},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {Predicting the time and effort for a software problem has long been a difficult task. We present an approach that automatically predicts the fixing effort, i.e., the person-hours spent on fixing an issue. Our technique leverages existing issue tracking systems: given a new issue report, we use the Lucene framework to search for similar, earlier reports and use their average time as a prediction. Our approach thus allows for early effort estimation, helping in assigning issues and scheduling stable releases. We evaluated our approach using effort data from the JBoss project. Given a sufficient number of issues reports, our automatic predictions are close to the actual effort; for issues that are bugs, we are off by only one hour, beating naive predictions by a factor of four.},
	keywords = {bug fixing, bug reports, effort estimation, jboss, lucene, prediction, time},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.13},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300001.pdf},
	author = {Weiss, Cathrin and Premraj, Rahul and Zimmermann, Thomas and Zeller, Andreas}
}
