@techreport{nagappan2005mining,
author = {Nagappan, Nachi and Ball, Thomas and Zeller, Andreas},
title = {Mining Metrics to Predict Component Failures},
year = {2005},
month = {November},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/mining-metrics-to-predict-component-failures/},
pages = {10},
number = {MSR-TR-2005-149},
}