You are here

Predicting failure-proneness in an evolving software product line

TitlePredicting failure-proneness in an evolving software product line
Publication TypeJournal Article
Year of Publication2013
AuthorsKrishnan, S, Strasburg, C, Lutz, RR, Goseva-Popstojanova, K, Dorman, KS
JournalInformation and Software Technology
Volume55
Pagination1479-1495
Date Published08
Type of ArticleArticle
ISBN Number0950-5849
Accession NumberWOS:000320685200009
KeywordsChange metrics, defect-density, defects, Failure-prone files, faults, models, Post-release, prediction, Reuse, Software product lines
Abstract

ares results from three different types of datasets with alternative data collection and prediction periods. Results: Our experiments with a variety of learners show that the difference between the performance of J48, used in this work, and the other top learners is not statistically significant. Furthermore, new results show that the effectiveness of classification significantly depends on the data collection period and prediction period. The study identifies change metrics that are prominent predictors across all four releases of all four products in the product line for the three different types of datasets. From the product line perspective, prediction of failure-prone files for the four products studied in the Eclipse product line shows statistically significant improvement in accuracy but not in recall across releases. Conclusion: As the product line matures, the learner performance improves significantly for two of the three datasets, but not for prediction of post-release failure-prone files using only pre-release change data. This suggests that it may be difficult to detect failure-prone files in the evolving product line. At least in part, this may be due to the continuous change, even for commonalities and high-reuse variation components, which we previously have shown to exist. (C) 2012 Elsevier B.V. All rights reserved.

DOI10.1016/j.infsof.2012.11.008
Custom 1

NSF