Thomas Zimmermann

Bio: Thomas Zimmermann is an academic researcher from Microsoft. The author has contributed to research in topics: Software development & Software. The author has an hindex of 68, co-authored 256 publications receiving 17984 citations. Previous affiliations of Thomas Zimmermann include University of Passau & Association for Computing Machinery.

Mining version histories to guide software changes

Abstract: We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed...." Given a set of existing changes, the mined association rules 1) suggest and predict likely further changes, 2) show up item coupling that is undetectable by program analysis, and 3) can prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict further locations to be changed; the best predictive power is obtained for changes to existing software. In our evaluation based on the history of eight popular open source projects, ROSE's topmost three suggestions contained a correct location with a likelihood of more than 70 percent.

When do changes induce fixes

Abstract: As a software system evolves, programmers make changes that sometimes cause problems. We analyze CVS archives for fix-inducing changes---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.

Mining version histories to guide software changes

Abstract: We apply data mining to version histories in order to guide programmers along related changes: "Programmers who changed these functions also changed. . . ". Given a set of existing changes, such rules (a) suggest and predict likely further changes, (b) show up item coupling that is indetectable by program analysis, and (c) prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict 26% of further files to be changed - and 15% of the precise functions or variables. The topmost three suggestions contain a correct location with a likelihood of 64%.

Predicting Defects for Eclipse

Abstract: We have mapped defects from the bug database of eclipse (one of the largest open-source projects) to source code locations. The resulting data set lists the number of pre- and post-release defects for every package and file in the eclipse releases 2.0, 2.1, and 3.0. We additionally annotated the data with common complexity metrics. All data is publicly available and can serve as a benchmark for defect prediction models.

Cross-project defect prediction: a large scale experiment on data vs. domain vs. process

Abstract: Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies focused on transferring prediction models from one project to another. In this paper, we study cross-project defect prediction models on a large scale. For 12 real-world applications, we ran 622 cross-project predictions. Our results indicate that cross-project prediction is a serious challenge, i.e., simply using models from projects in the same domain or with the same process does not lead to accurate predictions. To help software engineers choose models wisely, we identified factors that do influence the success of cross-project predictions. We also derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.

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