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, Tracking Concept Drift of Software Projects Using Defect Prediction Quality, Proceedings of the 6th IEEE Working Conference on Mining Software Repositories , May 2009, IEEE Computer Society. (inproceedings)
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?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.
, Improving Defect Prediction Using Temporal Features and Non Linear Models, Proceedings of the International Workshop on Principles of Software Evolution, September 2007, IEEE Computer Society. (inproceedings)
Predicting the defects in the next release of a large soft- ware system is a very valuable asset for the pro ject manger to plan her resources. In this paper we argue that temporal features (or aspects) of the data are central to prediction per- formance. We also argue that the use of non-linear models, as opposed to traditional regression, is necessary to uncover some of the hidden interrelationships between the features and the defects and maintain the accuracy of the prediction in some cases. Using data obtained from the CVS and Bugzilla reposito- ries of the Eclipse pro ject, we extract a number of temporal features, such as the number of revisions and number of re- ported issues within the last three months. We then use these data to predict both the location of defects (i.e., the classes in which defects will occur) as well as the number of reported bugs in the next month of the pro ject. To that end we use standard tree-based induction algorithms in compar- ison with the traditional regression. Our non-linear models uncover the hidden relationships be- tween features and defects, and present them in easy to un- derstand form. Results also show that using the temporal features our prediction model can predict whether a source ?le will have a defect with an accuracy of 99% (area under ROC curve 0.9251) and the number of defects with a mean absolute error of 0.019 (Spearman?s correlation of 0.96).