@proceedings {1501,
	title = {The Impact of Tangled Code Changes},
	year = {2013},
	month = {05/2013},
	abstract = {When interacting with version control systems, developers often commit unrelated or loosely related code changes
in a single transaction. When analyzing the version history, such
tangled changes will make all changes to all modules appear
related, possibly compromising the resulting analyses through
noise and bias. In an investigation of five open-source JAVA
projects, we found up to 15\% of all bug fixes to consist of multiple
tangled changes. Using a multi-predictor approach to untangle
changes, we show that on average at least 16.6\% of all source
files are incorrectly associated with bug reports. We recommend
better change organization to limit the impact of tangled changes.},
	keywords = {bias, data quality, history, java, mining software repositories, noise, tangled code changes, version control},
	url = {http://www.kim-herzig.de/wp-content/uploads/2013/03/msr2013-untangling.pdf},
	attachments = {https://flosshub.org/sites/flosshub.org/files/msr2013-untangling.pdf},
	author = {Kim Herzig and Zeller, Andreas}
}
@proceedings {1521,
	title = {It{\textquoteright}s Not a Bug, It{\textquoteright}s a Feature: How Misclassification Impacts Bug Prediction},
	year = {2013},
	month = {05/2013},
	pages = {392-401},
	abstract = {In a manual examination of more than 7,000 issue reports from the bug databases of five open-source projects, we found 33.8\% of all bug reports to be misclassified{\textemdash}that is, rather than referring to a code fix, they resulted in a new feature, an update to documentation, or an internal refactoring. This misclassification introduces bias in bug prediction models, confusing bugs and features: On average, 39\% of files marked as defective actually never had a bug. We discuss the impact of this misclassification on earlier studies and recommend manual data validation for future studies.},
	keywords = {bias, bug reports, data quality, mining software repositories, noise},
	author = {Kim Herzig and Sascha Just and Zeller, Andreas}
}
@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}
}
@conference {Sliwerski:2005:CIF:1083142.1083147,
	title = {When do changes induce fixes?},
	booktitle = {Proceedings of the 2005 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}05},
	year = {2005},
	pages = {24-28},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {As a software system evolves, programmers make changes that sometimes cause problems. We analyze CVS archives for fix-inducing changes{\textemdash}changes that lead to problems, indicated by fixes. We show how to automatically locate fix-inducing changes by linking a version archive (such as CVS) to a bug database (such as BUGZILLA). In a first investigation of the MOZILLA and ECLIPSE history, it turns out that fix-inducing changes show distinct patterns with respect to their size and the day of week they were applied.},
	keywords = {bugzilla, change analysis, cvs, eclipse, evolution, mozilla},
	isbn = {1-59593-123-6},
	doi = {http://doi.acm.org/10.1145/1082983.1083147},
	url = {http://doi.acm.org/10.1145/1082983.1083147},
	attachments = {https://flosshub.org/sites/flosshub.org/files/24WhenDoChanges.pdf},
	author = {Sliwerski, J and Zimmermann, Thomas and Zeller, Andreas}
}
