Sagar, Priyadarshni Suresh, AlOmar, Eman Abdullah, Mkaouer, Mohamed Wiem, Ouni, Ali et Newman, Christine D..
2021.
« Comparing commit messages and source code metrics for the prediction refactoring activities ».
Algorithms, vol. 14, nº 10.
Compte des citations dans Scopus : 12.
Prévisualisation |
PDF
Ouni-A-2021-23449.pdf - Version publiée Licence d'utilisation : Creative Commons CC BY. Télécharger (4MB) | Prévisualisation |
Résumé
Understanding how developers refactor their code is critical to support the design improvement process of software. This paper investigates to what extent code metrics are good indicators for predicting refactoring activity in the source code. In order to perform this, we formulated the prediction of refactoring operation types as a multi-class classification problem. Our solution relies on measuring metrics extracted from committed code changes in order to extract the corresponding features (i.e., metric variations) that better represent each class (i.e., refactoring type) in order to automatically predict, for a given commit, the method-level type of refactoring being applied, namely Move Method, Rename Method, Extract Method, Inline Method, Pull-up Method, and Push-down Method. We compared various classifiers, in terms of their prediction performance, using a dataset of 5004 commits and extracted 800 Java projects. Our main findings show that the random forest model trained with code metrics resulted in the best average accuracy of 75%. However, we detected a variation in the results per class, which means that some refactoring types are harder to detect than others.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Ouni, Ali |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 26 oct. 2021 20:14 |
| Dernière modification: | 16 oct. 2023 18:24 |
| URI: | https://espace2.etsmtl.ca/id/eprint/23449 |
Actions (Authentification requise)
 |
Dernière vérification avant le dépôt |