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

Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. We found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50-60% of the 20% most defect-prone components.

Reference

BibTeX Entry

@inproceedings{holschuh-icse-2009,
 title = "Predicting Defects in SAP Java Code: An Experience Report",
 author = "Tilman Holschuh and Markus Paeuser and Kim Herzig and Thomas Zimmermann and Rahul Premraj and Andreas Zeller",
 year = "2009",
 month = "May",
 booktitle = "Proceedings of the 31th International Conference on Software Engineering",
 location = "Vancouver, BC
}

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This thesis contains a lock flag. I am not allowed t publish this thesis neither completely nor in parts. The handout of this thesis to third persons requires a written approval of SAP.

One major down side of many defect prediction models is the frequent usability. As an example: A prediction model based on code metrics only, predicts the number of defects based upon the code complexity of each entity. Now, if the programmer fixes the bug, he might change only one line and therefore does not change the code complexity significantly. The complexity might even increase. Using the same defect prediction model again gives us the same result, because the basis of the defect prediction model did not changed or did not changed significantly.

To overcome such problems, we could use a dynamic approach. But most dynamic approaches cannot predict any defects before defects have been discovered.

The idea is to use different time related properties of a code entity to determine the age of this entity. The main assumption is that older entities have fewer defects. Defining the term older entities is the main task of this project.