Proceedings ArticleDOI
Where the bugs are
Thomas J. Ostrand,Elaine J. Weyuker,Robert M. Bell +2 more
- Vol. 29, Iss: 4, pp 86-96
TLDR
A negative binomial regression model using information from previous releases has been developed and used to predict the numbers of faults for a large industrial inventory system, and was extremely accurate.Abstract:
The ability to predict which files in a large software system are most likely to contain the largest numbers of faults in the next release can be a very valuable asset. To accomplish this, a negative binomial regression model using information from previous releases has been developed and used to predict the numbers of faults for a large industrial inventory system. The files of each release were sorted in descending order based on the predicted number of faults and then the first 20% of the files were selected. This was done for each of fifteen consecutive releases, representing more than four years of field usage. The predictions were extremely accurate, correctly selecting files that contained between 71% and 92% of the faults, with the overall average being 83%. In addition, the same model was used on data for the same system's releases, but with all fault data prior to integration testing removed. The prediction was again very accurate, ranging from 71% to 93%, with the average being 84%. Predictions were made for a second system, and again the first 20% of files accounted for 83% of the identified faults. Finally, a highly simplified predictor was considered which correctly predicted 73% and 74% of the faults for the two systems.read more
Citations
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Journal ArticleDOI
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
TL;DR: Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.
Proceedings ArticleDOI
Use of relative code churn measures to predict system defect density
Nachiappan Nagappan,Thomas Ball +1 more
TL;DR: A technique for early prediction of system defect density using a set of relative code churn measures that relate the amount of churn to other variables such as component size and the temporal extent of churn, which shows that while absolute measures of code chum are poor predictors of defect density, these measures are highly predictive of defectdensity.
Proceedings ArticleDOI
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
TL;DR: This paper studied cross-project defect prediction models on a large scale and identified factors that do influence the success of cross- project predictions, and derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.
Journal ArticleDOI
Classifying Software Changes: Clean or Buggy?
TL;DR: A description of the change classification approach, techniques for extracting features from the source code and change histories, a characterization of the performance of change classification across 12 open source projects, and an evaluation of the predictive power of different groups of features.
Journal ArticleDOI
Evaluating defect prediction approaches: a benchmark and an extensive comparison
TL;DR: The results indicate that, while some approaches perform better than others in a statistically significant manner, external validity in defect prediction is still an open problem, as generalizing results to different contexts/learners proved to be a partially unsuccessful endeavor.
References
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Journal ArticleDOI
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