A search-based training algorithm for cost-aware defect prediction

Panichella, A.; Alexandru, Carol V.; Panichella, Sebastiano; Bacchelli, A.; Gall, Harald C.

doi:10.1145/2908812.2908938

A search-based training algorithm for cost-aware defect prediction

Title

A search-based training algorithm for cost-aware defect prediction

Author

Panichella, A. (TU Delft Software Engineering)
Alexandru, Carol V. (University of Zürich)
Panichella, Sebastiano (University of Zürich)
Bacchelli, A. (TU Delft Software Engineering)
Gall, Harald C. (University of Zürich)

Date

2016

Abstract

Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each others' code changes. Developers are unlikely to devote the same effort to inspect each software artifact predicted to contain defects, since the effort varies with the artifacts' size (cost) and the number of defects it exhibits (effectiveness). We propose to use Genetic Algorithms (GAs) for training prediction models to maximize their cost-effectiveness. We evaluate the approach on two well-known models, Regression Tree and Generalized Linear Model, and predict defects between multiple releases of six open source projects. Our results show that regression models trained by GAs significantly outperform their traditional counterparts, improving the cost-effectiveness by up to 240%. Often the top 10% of predicted lines of code contain up to twice as many defects.

Subject

Defect prediction
Genetic algorithm
Machine learning

To reference this document use:

http://resolver.tudelft.nl/uuid:67912115-0188-47d9-9923-67b794b83f8a

DOI

https://doi.org/10.1145/2908812.2908938

Publisher

Association for Computing Machinery (ACM), New York, NY

ISBN

978-1-4503-4206-3

Source

Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2016