Moussa, Rebecca;
Guizzo, Giovani;
Sarro, federica;
(2022)
MEG: Multi-objective Ensemble Generation for Software Defect Prediction.
In:
Proceedings of the 2022 International Symposium on Empirical Software Engineering and Measurement (ESEM2022).
Association for Computing Machinery (ACM): New York, NY, USA.
(In press).
Preview |
Text
esem22-23.pdf - Accepted Version Download (701kB) | Preview |
Abstract
Background: Defect Prediction research aims at assisting software engineers in the early identification of software defect during the development process. A variety of automated approaches, ranging from traditional classification models to more sophisticated learning approaches, have been explored to this end. Among these, recent studies have proposed the use of ensemble prediction models (i.e., aggregation of multiple base classifiers) to build more robust defect prediction models. / Aims: In this paper, we introduce a novel approach based on multi-objective evolutionary search to automatically generate defect prediction ensembles. Our proposal is not only novel with respect to the more general area of evolutionary generation of ensembles, but it also advances the state-of-the-art in the use of ensemble in defect prediction. / Method: We assess the effectiveness of our approach, dubbed as Multi-objective Ensemble Generation (MEG), by empirically benchmarking it with respect to the most related proposals we found in the literature on defect prediction ensembles and on multi-objective evolutionary ensembles (which, to the best of our knowledge, had never been previously applied to tackle defect prediction). / Result: Our results show that MEG is able to generate ensembles which produce similar or more accurate predictions than those achieved by all the other approaches considered in 73% of the cases (with favourable large effect sizes in 80% of them). / Conclusions: MEG is not only able to generate ensembles that yield more accurate defect predictions with respect to the benchmarks considered, but it also does it automatically, thus relieving the engineers from the burden of manual design and experimentation.
| Type: | Proceedings paper |
|---|---|
| Title: | MEG: Multi-objective Ensemble Generation for Software Defect Prediction |
| Event: | 2022 International Symposium on Empirical Software Engineering and Measurement (ESEM2022) |
| Location: | Helsinki, Finland |
| Dates: | 18 Sep 2022 - 23 Sep 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3544902.3546255 |
| Publisher version: | https://www.esem-conferences.org/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Defect Prediction, Search-Based Software Engineering, Multi-Objective Optimisation, Hyper-Heuristic, Empirical Study |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152591 |
Archive Staff Only
 |
View Item |
