Machine Learning for Change-Prone Class Prediction: A History-Based Approach-Reference-Cited by-同舟云学术

Machine Learning for Change-Prone Class Prediction: A History-Based Approach

Published:2022-10-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the XXXVI Brazilian Symposium on Software Engineering
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Author:

Silva RogéRio De Carvalho¹,Farah Paulo Roberto²,Vergilio Silvia Regina¹

Affiliation:

1. Department of Informatics, Federal University of Paraná, Brazil

2. Department of Informatics, Federal University of Paraná, Brazil and Software Engineering Department, Santa Catarina State University - UDESC, Brazil

Funder

CAPES and CNPq Brazil

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3555228.3555249

Reference57 articles.

1. Mojeeb Al-Khiaty Radwan Abdel-Aal and Mahmoud Elish. 2017. Abductive network ensembles for improved prediction of future change-prone classes in object-oriented software.International Arab Journal of Information Technology (IAJIT) 14 6(2017). Mojeeb Al-Khiaty Radwan Abdel-Aal and Mahmoud Elish. 2017. Abductive network ensembles for improved prediction of future change-prone classes in object-oriented software.International Arab Journal of Information Technology (IAJIT) 14 6(2017).

2. Dynamic coupling measurement for object-oriented software

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

4. A study of the behavior of several methods for balancing machine learning training data

5. Understanding cost drivers of software evolution: a quantitative and qualitative investigation of change effort in two evolving software systems

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1. Machine Learning for Change-Prone Class Prediction: A History-Based Approach;Proceedings of the XXXVI Brazilian Symposium on Software Engineering;2022-10-05