Software archives mining deals with the automated extraction, collection, and abstraction of data from the information generated during the software development process (e.g. source code archives, bug tracking systems, etc.). This seminar (7 CP) introduces the notion of software archives and teaches recent software archives mining techniques. More details here

Given a large body of code, how do we know where to focus our quality assurance effort? By mining the software’s defect history, we can automatically learn which code features correlated with defects in the past—and leverage these correlations for new predictions: “In the past, high inheritance depth was an indicator of a high number of defects. Since this new component also has a high inheritance depth, let us test it thoroughly”. Such history-based approaches work best if the new component is similar to the components learned from. But how does learning from history perform for projects with high variability between components? We ran a study on two SAP products involving a wide spectrum of functionality. We found that learning and predicting was accurate at package level, but not at product level. These results suggest that to learn from past defects, one should separate the product into component clusters with similar functionality, and make separate predictions for each cluster. Our initial approaches to form such clusters automatically, based on similarity of metrics, showed promising accuracy.

Reference

BibTeX Entry

@techreport{herzig-abap-prediction-2008,
 title = "Predicting Defects in SAP Products: A Replicated Study",
 author = "Kim Herzig and Rahul Premraj and Thomas Zimmermann and Andreas Zeller and Juergen Heymann",
 institution = "Software Engineering Chair, Saarland University"
}

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Software products and projects can become very large and still grow over time. Building one prediction model for a whole software product might be easy but might also limit the prediction accuracy.

Different parts of a software product have different duties (GUI, database, kernel,…). We found out that for each of these different code zones there exist different code characteristics that matter when it comes to defect prediction. The main idea is to cluster code entities by their software duty. Currently we are investigating whether defect prediction taking advantage of such clustering techniques have a higher prediction accuracy than defect prediction models using no clustering technique.