Authors :
Neha Sah
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/6n9enddp
Scribd :
https://tinyurl.com/4fzh3ntj
DOI :
https://doi.org/10.38124/ijisrt/25mar1963
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This research paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in
the field of software testing automation. By examining key advancements in test case generation, defect detection, regression
testing, and test optimization, this study highlights how AI-driven approaches enhance the efficiency, accuracy, and coverage
of automated software testing. Furthermore, the paper addresses the challenges associated with implementing AI/ML in
testing workflows, such as data dependency, explainability, and ethical considerations. The findings emphasize the importance
of integrating AI and ML in QA practices to ensure adaptive and intelligent testing solutions.
Keywords :
Software Testing, Test Automation, Artificial Intelligence, Machine Learning, Test Case Generation, Defect Detection, Regression Testing.
References :
- M. Utting, A. Pretschner, and B. Legeard, “A taxonomy of model-based testing approaches,” Software Testing, Verification and Reliability, 2012.
- T. Kanewala and J.M. Bieman, “Using machine learning techniques to detect metamorphic relations for programs without test oracles,” Software Testing, 2015.
- S. Yoo and M. Harman, “Regression testing minimization, selection and prioritization: A survey,” Software Testing, 2012.
- DeepCode: https://www.deepcode.ai
- Amazon CodeGuru: https://aws.amazon.com/codeguru/
- Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” IEEE Transactions on Software Engineering, 2011.
This research paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in
the field of software testing automation. By examining key advancements in test case generation, defect detection, regression
testing, and test optimization, this study highlights how AI-driven approaches enhance the efficiency, accuracy, and coverage
of automated software testing. Furthermore, the paper addresses the challenges associated with implementing AI/ML in
testing workflows, such as data dependency, explainability, and ethical considerations. The findings emphasize the importance
of integrating AI and ML in QA practices to ensure adaptive and intelligent testing solutions.
Keywords :
Software Testing, Test Automation, Artificial Intelligence, Machine Learning, Test Case Generation, Defect Detection, Regression Testing.
