A systematic review of software fault prediction studies - Catal, Cagatay; Diri, Banu (PERGAMON-ELSEVIER SCIENCE LTD, 2009)
This paper provides a systematic review of previous software fault prediction studies with a specific focus on metrics, methods, and datasets. The review uses 74 software fault prediction papers in I I journals and several conference proceedings. According to the review results, the usage percentage of public datasets increased significantly and the usage percentage of machine learning algorithms increased slightly since 2005. In addition, method-level metrics are still the most dominant metrics in fault prediction research area and machine learning algorithms are still the most popular methods for fault prediction. Researchers working on software fault prediction area should continue to use public datasets and machine learning algorithms to build better fault predictors. The usage percentage of class-level is beyond acceptable levels and they should be used much more than they are now in order to predict the faults earlier in design phase of software life cycle. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Machine learning; Automated fault prediction models; Public datasets; Method-level metrics; Expert systems; METRICS; PRONENESS; Computer Science; Engineering; Operations Research & Management Science