Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy
Author:
Affiliation:
1. Department of Computer Science, Mustapha Stambouli University, Mascara, Algeria
2. Department of Software Engineering, Prince Sultan University, Riyadh, Saudi Arabia
Funder
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10526245.pdf?arnumber=10526245
Reference41 articles.
1. Predicting co‐change probability in software applications using historical metadata
2. Evaluating the Impact of Possible Dependencies on Architecture-Level Maintainability
3. Towards automatically identifying the co‐change of production and test code
4. Data-driven prediction of change propagation using Dependency Network
5. FCP2Vec: Deep Learning-Based Approach to Software Change Prediction by Learning Co-Changing Patterns from Changelogs
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