Software Change Prediction with Homogeneous Ensemble Learners on Large Scale Open-Source Systems
| Autor: | Megha Khanna, Diksha Mehra, Srishti Priya |
|---|---|
| Přispěvatelé: | Sri Guru Gobind Singh College of Commerce, University of Delhi, Davide Taibi, Valentina Lenarduzzi, Terhi Kilamo, Stefano Zacchiroli, TC 2, WG 2.13 |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
media_common.quotation_subject Ensemble learners Software change prediction 02 engineering and technology Machine learning computer.software_genre Software 0202 electrical engineering electronic engineering information engineering Quality (business) [INFO]Computer Science [cs] Empirical validation media_common business.industry Change prediction Scale (chemistry) Software development 020207 software engineering Popularity Random forest Large-scale OSS Homogeneous 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | IFIP Advances in Information and Communication Technology 17th IFIP International Conference on Open Source Systems (OSS) 17th IFIP International Conference on Open Source Systems (OSS), May 2021, Lathi/virtual event, Finland. pp.68-86, ⟨10.1007/978-3-030-75251-4_7⟩ IFIP Advances in Information and Communication Technology ISBN: 9783030752507 OSS |
| DOI: | 10.1007/978-3-030-75251-4_7⟩ |
| Popis: | International audience; Customizability, extensive community support and ease of availability have led to the popularity of Open-Source Software (OSS) systems. However, maintenance of these systems is a challenge especially as they become considerably large and complex with time. One possible method of ensuring effective quality in large scale OSS is the adoption of software change prediction models. These models aid in identifying change-prone parts in the early stages of software development, which can then be effectively managed by software practitioners. This study extensively evaluates eight Homogeneous Ensemble Learners (HEL) for developing software change prediction models on five large scale OSS datasets. HEL, which integrate the outputs of several learners of the same type are known to generate improved results than other non-ensemble classifiers. The study also statistically compares the results of the models developed by HEL with ten non-ensemble classifiers. We further assess the change in performance of HEL for developing software change prediction models by substituting their default base learners with other classifiers. The results of the study support the use of HEL for developing software change prediction models and indicate Random Forest as the best HEL for the purpose. |
| Databáze: | OpenAIRE |
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