In software development, every change induces a risk. What happens if code changes again and again in some period of time? In an empirical study on Windows Vista, we found that the features of such change bursts have the highest predictive power for defect-prone components. With precision and recall values well above 90%, change bursts significantly improve upon earlier predictors such as complexity metrics, code churn, or organizational structure. As they only rely on version history and a controlled change process, change bursts are straight-forward to detect and deploy.

Reference

BibTeX Entry

@inproceedings{nagappan-issre-2010,
 title = "Change Bursts as Defect Predictors",
 author = "Nachiappan Nagappan and Andreas Zeller and Thomas Zimmermann and Kim Herzig and Brendan Murphy",
 year = "2010",
 month = "November",
 booktitle = "Proceedings of the 21st IEEE International Symposium on Software Reliability Engineering",
 location = "San Jose, California
}

Download PDF version

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