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An Adaptive Risk-Aware Machine Learning Framework for Cost-Effective Regression Test Case Prioritization
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: 10.64388/IREV10I4-1724398
Abstract
Regression testing is essential for identifying defects after software changes, but running all test cases can be time-consuming and costly. This paper proposes an Adaptive Risk-Aware Machine Learning Framework for Cost-Effective Regression Test Case Prioritization. The framework uses a Random Forest classifier to predict test case risk based on change impact, historical failures, code coverage, execution cost, and recent results. It dynamically prioritizes test cases using predicted risk, execution cost, and feedback from prior executions. We evaluated the approach using APFD, APFDc, fault detection rate, and execution cost. Experimental results show an APFD of 0.7173 and an APFDc of 0.7987, demonstrating improved test case prioritization compared with the selected baseline techniques.
Keywords
adaptive testing; cost-effective testing; machine learning; regression testing; test case prioritization
References
[1] Elbaum S, Malishevsky AG, Rothermel G. Test Case Prioritization: A Family of Empirical Studies. IEEE Transactions on Software Engineering. 2002;28(2):159–182. https://doi.org/10.1109/32.988497
[2] Rothermel G, Untch RH, Chu C, Harrold MJ. Prioritizing Test Cases for Regression Testing. IEEE Transactions on Software Engineering. 2001;27(10):929–948. https://doi.org/10.1109/32.962562
[3] Huang YC, Huang CY, Chang JR, Chen TY. Design and Analysis of Cost-Cognizant Test Case Prioritization Using Genetic Algorithm with Test History. In: Proceedings of the 2010 IEEE 34th Annual Computer Software and Applications Conference (COMPSAC), Seoul, South Korea. 2010. p. 413–418. https://doi.org/10.1109/COMPSAC.2010.66
[4] Pan R, Bagherzadeh M, Ghaleb TA, Briand LC. Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review. Empirical Software Engineering. 2022;27(2):29. https://doi.org/10.1007/s10664-021-10066-6
[5] Breiman L. Random Forests. Machine Learning. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324
[6] Elbaum S, Malishevsky AG, Rothermel G. Incorporating Varying Test Costs and Fault Severities into Test Case Prioritization. In: Proceedings of the 23rd International Conference on Software Engineering (ICSE). 2001.
How to cite this paper
@article{1724398,
author = {Prof. Priya Sawant, Prof. Jyoti Tale},
title = {An Adaptive Risk-Aware Machine Learning Framework for Cost-Effective Regression Test Case Prioritization},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4273-4279},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724398.pdf},
abstract = {Regression testing is essential for identifying defects after software changes, but running all test cases can be time-consuming and costly. This paper proposes an Adaptive Risk-Aware Machine Learning Framework for Cost-Effective Regression Test Case Prioritization. The framework uses a Random Forest classifier to predict test case risk based on change impact, historical failures, code coverage, execution cost, and recent results. It dynamically prioritizes test cases using predicted risk, execution cost, and feedback from prior executions. We evaluated the approach using APFD, APFDc, fault detection rate, and execution cost. Experimental results show an APFD of 0.7173 and an APFDc of 0.7987, demonstrating improved test case prioritization compared with the selected baseline techniques.},
keywords = {adaptive testing; cost-effective testing; machine learning; regression testing; test case prioritization},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724398}
}