Mining metrics to predict component failures

Nachiappan Nagappan, Thomas Ball, Andreas Zeller
2006 Proceeding of the 28th international conference on Software engineering - ICSE '06  
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
more » ... se 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.
doi:10.1145/1134285.1134349 dblp:conf/icse/NagappanBZ06 fatcat:5z7xsgy2tjhrtpdly6mpohbdwm