Mining metrics to predict component failures

• DOI: 10.1145/1134285.1134349
• Publication Date: 2006-07-24T16:53:01Z
• Authors: Nachiappan Nagappan; Thomas Ball; Andreas Zeller
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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.