Smart prediction for refactorings in the software test code
Author:
Affiliation:
1. Federal University of Bahia, Brazil
2. Federal University of Ceará, Brazil
3. Federal University of Lavras, Brazil
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3474624.3477070
Reference29 articles.
1. Wajdi Aljedaani Anthony Peruma Ahmed Aljohani Mazen Alotaibi Mohamed Wiem Mkaouer Ali Ouni Christian D. Newman Abdullatif Ghallab and Stephanie Ludi. 2021. Test Smell Detection Tools: A Systematic Mapping Study. arxiv:2104.14640 [cs.SE] Wajdi Aljedaani Anthony Peruma Ahmed Aljohani Mazen Alotaibi Mohamed Wiem Mkaouer Ali Ouni Christian D. Newman Abdullatif Ghallab and Stephanie Ludi. 2021. Test Smell Detection Tools: A Systematic Mapping Study. arxiv:2104.14640 [cs.SE]
2. Maurício Aniche Erick Maziero Rafael Durelli and Vinicius Durelli. 2020. The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring. arxiv:2001.03338 [cs.SE] Maurício Aniche Erick Maziero Rafael Durelli and Vinicius Durelli. 2020. The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring. arxiv:2001.03338 [cs.SE]
3. Machine learning techniques for code smell detection: A systematic literature review and meta-analysis
4. Are test smells really harmful? An empirical study
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