Mining Metrics to Predict Component Failures

Nachiappan Nagappan, Thomas Ball, and Andreas Zeller

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.

Details

Publication typeTechReport
NumberMSR-TR-2005-149
Pages10
InstitutionMicrosoft Research
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