%0 Conference Proceedings %B 35th Int'l Conference on Software Engineering (ICSE 2013) %D 2013 %T It’s Not a Bug, It’s a Feature: How Misclassification Impacts Bug Prediction %A Kim Herzig %A Sascha Just %A Zeller, Andreas %K bias %K bug reports %K data quality %K mining software repositories %K noise %X 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—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. %B 35th Int'l Conference on Software Engineering (ICSE 2013) %P 392-401 %8 05/2013 %0 Journal Article %J IEEE Transactions on Software Engineering %D 2010 %T What Makes a Good Bug Report? %A Zimmermann, Thomas %A Premraj, Rahul %A Bettenburg, Nicolas %A Sascha Just %A Schroter, Adrian %A Weiss, Cathrin %K bug report %K Survey %X 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 31–48% of bug reports accurately. %B IEEE Transactions on Software Engineering %I IEEE Computer Society %C Los Alamitos, CA, USA %V 36 %P 618-643 %U http://dl.acm.org/citation.cfm?id=1453146 %R http://doi.ieeecomputersociety.org/10.1109/TSE.2010.63 %> https://flosshub.org/sites/flosshub.org/files/bettenburg-fse-2008.pdf