@conference {926,
	title = {Tracking concept drift of software projects using defect prediction quality},
	booktitle = {2009 6th IEEE International Working Conference on Mining Software Repositories (MSR)2009 6th IEEE International Working Conference on Mining Software Repositories},
	year = {2009},
	pages = {51 - 60},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, BC, Canada},
	abstract = {Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we investigate the reasons why the prediction quality is so fluctuating due to the altering nature of the bug (or defect) fixing process. Therefore, we adopt the notion of a concept drift, which denotes that the defect prediction model has become unsuitable as set of influencing features has changed - usually due to a change in the underlying bug generation process (i.e., the concept). We explore four open source projects (Eclipse, OpenOffice, Netbeans and Mozilla) and construct file-level and project-level features for each of them from their respective CVS and Bugzilla repositories. We then use this data to build defect prediction models and visualize the prediction quality along the time axis. These visualizations allow us to identify concept drifts and - as a consequence - phases of stability and instability expressed in the level of defect prediction quality. Further, we identify those project features, which are influencing the defect prediction quality using both a tree induction-algorithm and a linear regression model. Our experiments uncover that software systems are subject to considerable concept drifts in their evolution history. Specifically, we observe that the change in number of authors editing a file and the number of defects fixed by them contribute to a project{\textquoteright}s concept drift and therefore influence the defect prediction quality. Our findings suggest that project managers using defect prediction models for decision making should be aware of the actual phase of stability or instability due to a potential concept drift.},
	keywords = {bugzilla, cvs, defect prediction, eclipse, mozilla, netbeans, openoffice},
	isbn = {978-1-4244-3493-0},
	doi = {10.1109/MSR.2009.5069480},
	attachments = {https://flosshub.org/sites/flosshub.org/files/51MSR2009_0111_Ekanayake_Jayalath.pdf},
	author = {Ekanayake, Jayalath and Tappolet, Jonas and Gall, Harald C. and Bernstein, Abraham}
}
@conference {Zhang:2008:ISG:1370750.1370785,
	title = {An initial study of the growth of eclipse defects},
	booktitle = {Proceedings of the 2008 international working conference on Mining software repositories},
	series = {MSR {\textquoteright}08},
	year = {2008},
	pages = {141{\textendash}144},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We analyze the Eclipse defect data from June 2004 to November 2007, and find that the growth of the number of defects can be well modeled by polynomial functions. Furthermore, we can predict the number of future Eclipse defects based on the nature of defect growth.},
	keywords = {bug reports, defect growth model, defect prediction, eclipse, msr challenge, polynomial regression},
	isbn = {978-1-60558-024-1},
	doi = {http://doi.acm.org/10.1145/1370750.1370785},
	url = {http://doi.acm.org/10.1145/1370750.1370785},
	author = {Zhang, Hongyu}
}
@conference {Knab:2006:PDD:1137983.1138012,
	title = {Predicting defect densities in source code files with decision tree learners},
	booktitle = {Proceedings of the 2006 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}06},
	year = {2006},
	pages = {119{\textendash}125},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out to derive reasonable results the sheer amount of data and the problem context of defect prediction demand sophisticated analysis such as provided by current data mining and machine learning techniques.In this work we focus on defect density prediction and present an approach that applies a decision tree learner on evolution data extracted from the Mozilla open source web browser project. The evolution data includes different source code, modification, and defect measures computed from seven recent Mozilla releases. Among the modification measures we also take into account the change coupling, a measure for the number of change-dependencies between source files. The main reason for choosing decision tree learners, instead of for example neural nets, was the goal of finding underlying rules which can be easily interpreted by humans. To find these rules, we set up a number of experiments to test common hypotheses regarding defects in software entities. Our experiments showed, that a simple tree learner can produce good results with various sets of input data.},
	keywords = {change analysis, data mining, decision tree learner, defect density, defect prediction, mozilla, prediction, release history, scm, source code, version control},
	isbn = {1-59593-397-2},
	doi = {http://doi.acm.org/10.1145/1137983.1138012},
	url = {http://doi.acm.org/10.1145/1137983.1138012},
	attachments = {https://flosshub.org/sites/flosshub.org/files/119Predicting.pdf},
	author = {Knab, Patrick and Pinzger, Martin and Bernstein, Abraham}
}
