An Assessment of Machine Learning Algorithms and Models for Prediction of Change-Prone Java Methods-Reference-Cited by-同舟云学术

An Assessment of Machine Learning Algorithms and Models for Prediction of Change-Prone Java Methods

Published:2023-09-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the XXXVII Brazilian Symposium on Software Engineering
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Author:

Farah Paulo Roberto¹^ORCID,Silva Rogério²^ORCID,Vergilio Silvia²^ORCID

Affiliation:

1. Software Engineering Department, UDESC, Brazil and PPGInf, UFPR, Brazil

2. PPGInf, UFPR, Brazil

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613372.3613395

Reference37 articles.

1. Abductive network ensembles for improved prediction of future change-prone classes in object-oriented software.Int;Al-Khiaty Mojeeb;Arab Journal of Inf. Techn. (IAJIT),2017

2. Hirohisa Aman , Sousuke Amasaki , Takashi Sasaki , and Minoru Kawahara . 2015 . Empirical Analysis of Change-Proneness in Methods Having Local Variables with Long Names and Comments. In 2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) ( Beijing, China). 1–4. Hirohisa Aman, Sousuke Amasaki, Takashi Sasaki, and Minoru Kawahara. 2015. Empirical Analysis of Change-Proneness in Methods Having Local Variables with Long Names and Comments. In 2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (Beijing, China). 1–4.

3. Maurício Aniche. 2015. Java code metrics calculator (CK). Available in https://github.com/mauricioaniche/ck/. Maurício Aniche. 2015. Java code metrics calculator (CK). Available in https://github.com/mauricioaniche/ck/.

4. The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring

5. A hierarchical model for object-oriented design quality assessment