Mining Metrics to Predict Component Failures - ICSE 2006
by Nachiappan Nagappan, Thomas Ball, Andreas Zeller

November 2005.

Digital Library via DOI: 10.1145/1134285.1134349 - Local copy: Download as PDF file.

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.

Keywords

BibTeX Entry

@inproceedings{nagappan-icse-2006,
    title = "Mining Metrics to Predict Component Failures",
    author = "Nachiappan Nagappan and Thomas Ball and Andreas Zeller",
    year = "2005",
    month = nov,
    location = "Shanghai, China",
    doi = "10.1145/1134285.1134349",
}

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