Mining Metrics to Predict Component Failures
- ICSE 2006
by
Nachiappan Nagappan, Thomas Ball, Andreas Zeller
November 2005.
Digital Library via DOI: 10.1145/1134285.1134349 - Local copy: Download as PDF file.
Abstract
What is it that makes software fail? In an empirical study of the post-release defect history of five Microsoft software systems, we found that failure-prone software entities are statistically correlated with code complexity measures. However, there is no single set of complexity metrics that could act as a universally best defect predictor. Using principal component analysis on the code metrics, we built regression models that accurately predict the likelihood of post-release defects for new entities. The approach can easily be generalized to arbitrary projects; in particular, predictors obtained from one project can also be significant for new, similar projects.
Keywords
- empirical study
- bug database
- complexity metrics
- principal component analysis
- regression model
BibTeX Entry
@inproceedings{nagappan-icse-2006, title = "Mining Metrics to Predict Component Failures", author = "Nachiappan Nagappan and Thomas Ball and Andreas Zeller", year = "2005", month = nov, location = "Shanghai, China", doi = "10.1145/1134285.1134349", }