International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 7 | Year 2025 | Article Id. IJCTT-V73I7P108 | DOI : https://doi.org/10.14445/22490183/IJCTT-V73I7P108

Automation for the Future: Harnessing AI and ML to Reshape Software Testing and Maintenance

Chandrasekhar Rao Katru, Sandip J. Gami, Kevin N. Shah

Received Revised Accepted Published
02 Jun 2025 25 Jun 2025 17 Jul 2025 29 Jul 2025

Citation :

Chandrasekhar Rao Katru, Sandip J. Gami, Kevin N. Shah, "Automation for the Future: Harnessing AI and ML to Reshape Software Testing and Maintenance," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 7, pp. 63-72, 2025. Crossref, https://doi.org/10.14445/22490183/IJCTT-V73I7P108

Abstract

Within the software development lifecycle, software testing and maintenance form critical components that require significant allocation of resources. Routine processes within this area often face challenges like automation, error detection, and complex modern software systems. The automation of repetitive work processes, detection of failure patterns, and making smart decisions based on available data is now possible due to the advances of Artificial Intelligence (AI) and Machine Learning (ML). The purpose of this review is to revisit the methodologies, the available tools, and the challenges that AI and ML pose in software testing and maintenance. It integrates known processes of testing and automation of AI, involving the accuracy of defects, the generation of test cases, and regression optimization. The results of the study provide evidence of improvement in the efficiency of software testing, accuracy of defect detection, and software maintenance turnaround time. AI ethics were explained, in addition to the use of quality data from datasets to ensure the AI system is not biased, is non-discriminatory, and reliable in the results of the tests.

Keywords

Artificial Intelligence, Machine Learning, Software Testing, Software Maintenance, Test Automation, Defect Prediction, Natural Language Processing, Predictive Analytics, Continuous Integration, Reinforcement Learning, Test Case Generation.

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