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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

AI-driven green testing : Optimizing efficiency and sustainability in software testing

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pp. 1347–1356Vol. 46Issue 4-BMay 2025DOI: 10.47974/JIOS-2001XML
Received:
09 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2001
Pages:
1347–1356

Abstract

Software has become integral to daily life, continually expanding with increasingly complex features to meet growing expectations. However, managing these complexities—from understanding application dependencies to ensuring reliability through rigorous testing—poses significant challenges.  For organizations, delivering robust and dependable software is crucial for maintaining business success and reputation. Therefore, a significant portion of the software development life cycle is dedicated to testing. Engineers often write thousands of test cases to validate new features and prevent regressions. Despite these efforts, current regression testing methods are inefficient, as they often require developers to run all test cases irrespective of whether the code paths have been altered.  This paper proposes an AI-driven approach to optimize regression testing. By analyzing specific trends, the AI identifies and prioritizes the most relevant test cases, thereby reducing execution time and resource consumption. This approach promises to mitigate inefficiencies associated with traditional testing methods, offering a more cost-effective and timely solution for software development cycles.

Keywords

AI-driven testingRegression test optimizationTest case prioritizationCost effective testingGreen testing

Subject Classifications

68M15

References

[1] M. Khatibsyarbini, M. H. H. A. Bakar, M. Z. I. Abdul Karim, and M. I. Ghani, “Test case prioritization approaches in regression testing: A systematic literature review,” Information and Software Technology, vol. 93, pp. 74–93 (2018).
[2] F. Martinelli and I. Matteucci, “A framework for automatic generation of security controller,” Software Testing, Verification and Reliability, vol. 22, no. 8, pp. 563–582 (Dec. 2012).
[3] O. Legunsen, A. Shi, and D. Marinov, “STARTS: STAtic regression test selection,” in Proc. 32nd IEEE/ACM Int. Conf. Automated Software Engineering (ASE), Urbana-Champaign, IL, USA, pp. 949–954 (2017).
[4] O. Legunsen, A. Shi, and D. Marinov, “STARTS: STAtic regression test selection,” in Proc. 32nd IEEE/ACM Int. Conf. Automated Software Engineering (ASE), Urbana-Champaign, IL, USA, pp. 949–954 (2017).
[5] K. Kaur and V. Chopra, “Test case prioritization using genetic algorithm,” International Journal of Computer Science and Mobile Computing, vol. 5, no. 6, pp. 488–494 (2016).
[6] A. K. Arumugam, “Software testing techniques & new trends,” International Journal of Engineering Research & Technology (IJERT), vol. 8, no. 12 (2019).
[7] R. Khankhoje, “AI in test automation: Overcoming challenges, embracing imperatives,” International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), vol. 13, no. 1, pp. 1–10 (2024).
[8] M. Khan, A. Ghafoor, J. W. Byun, and M. S. Kim, “Hadoop performance modeling for job estimation and resource provisioning,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp. 441–454 (Feb. 2015).
[9] S. Biswas, R. Mall, A. Mohanty, and S. Sukumaran, “Regression test selection techniques: A survey,” Informatica, vol. 35, no. 3, pp. 289–321 (2011).
[10] R. Verdecchia, A. Procaccianti, and P. Lago, “Green IT and green software,” IEEE Software, vol. 38, no. 6, pp. 7–15 (Nov./Dec. 2021).
[11] C. Ebert, D. Bajaj, and M. Weyrich, “Testing software systems,” IEEE Software, vol. 39, no. 4, pp. 8–17 (Jul./Aug. 2022).
[12] S. Yoo and M. Harman, “Regression testing minimization, selection and prioritization: A survey,” Software Testing, Verification and Reliability, vol. 22, no. 2, pp. 67–120 (Mar. 2012).

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