AuthorsE. Arisholm and L. Briand
EditorsMendes and W. Maldonado
TitlePredicting Fault-Prone Components in a Java Legacy System
AfilliationSoftware Engineering, Software Engineering
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2006
Conference Name5th ACM-IEEE International Symposium on Empirical Software Engineering (ISESE), Rio de Janeiro, Brazil, September 21-22
Pagination8-17
Date PublishedSeptember
PublisherACM
ISBN Number1-59593-218-6
Abstract

This paper reports on the construction and validation of fault-proneness prediction models in the context of an object-oriented, evolving, legacy system. The goal is to help QA engineers focus their limited verification resources on parts of the system likely to contain faults. A number of measures including code quality, class structure, changes in class structure, and the history of class-level changes and faults are included as candidate predictors of class fault-proneness. A cross-validated classification analysis shows that the obtained model has less than 20% of false positives and false negatives, respectively. However, as shown in this paper, statistics regarding the classification accuracy tend to inflate the potential usefulness of the fault-proneness prediction models. We thus propose a simple and pragmatic methodology for assessing the cost-effectiveness of the predictions to focus verification effort. On the basis of the cost-effectiveness analysis we show that change and fault data from previous releases is paramount to developing a practically useful prediction model. When our model is applied to predict faults in a new release, the estimated potential savings in verification effort is about 29%. In contrast, the estimated savings in verification effort drops to 0% when history data is not included.

Citation KeyArisholm.2006.4