Mining Metrics To Predict Component Failures
Stc
Date: 2006-01-18
Time: 10:00
Room: BBL room 471
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.
