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Mining metrics to predict component failures (2006)
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| Venue: | In Proc. 28 th Int’l Conf. on Softw. Eng |
| Citations: | 199 - 10 self |
BibTeX
@INPROCEEDINGS{Ball06miningmetrics,
author = {Thomas Ball and Andreas Zeller},
title = {Mining metrics to predict component failures},
booktitle = {In Proc. 28 th Int’l Conf. on Softw. Eng},
year = {2006},
pages = {452--461}
}
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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.
Keyphrases
component failure microsoft software system complexity metric code complexity measure post-release defect defect predictor single set similar project new entity arbitrary project failure-prone software entity principal component analysis regression model post-release defect history code metric empirical study
