Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1134285.1134349 | ICSE |
Keywords | Field | DocType |
design,distribution,principal component,empirical study,principal component analysis,software systems,regression model | Halstead complexity measures,Data mining,Regression analysis,Computer science,Cyclomatic complexity,Software system,Software,Artificial intelligence,Machine learning,Empirical research,Software regression,Principal component analysis | Conference |
ISBN | Citations | PageRank |
1-59593-375-1 | 402 | 14.41 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nachiappan Nagappan | 1 | 4602 | 190.30 |
| Thomas Ball | 2 | 4969 | 365.11 |
| Andreas Zeller | 3 | 5697 | 303.71 |