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Mining Metrics To Predict Component Failures
Stc
Date: 2006-01-18 Time: 10:00 Room: BBL room 471 ----+++ Speaker: [[http://www.st.cs.uni-sb.de/~zeller/][Prof. Dr. Andreas Zeller]] ----+++ Title: Mining Metrics to Predict Component Failures ----+++ 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. Andreas Zeller is a software engineering professor at Saarland University, Germany; he researches large software systems and their processes, especially the analysis of why these fai. The present work was carried out as a visiting researcher with the Testing, Verification and Measurement Group at Microsoft Research.
