Advancements and Challenges in Using AI and ML to Improve API Testing Efficiency, Coverage, and Effectiveness
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P111Keywords:
Machine learning, API testing, test automation, test prioritization, anomaly detection, reinforcement learningAbstract
The proliferation of microservices and API-centered software designs has increased the complexity and breadth of the testing needed in order to achieve reliable, high-performance systems exponentially. Classical API testing techniques, such as scripted automation or simply manual validation, lack the scalability, speed, and coverage required to succeed in the contemporary development context. To overcome these shortcomings, the increasing use of artificial intelligence (AI) and machine learning (ML) is being used within the API testing lifecycle. This paper discusses the recent development of API testing using AI/ML and its techniques, including intelligent generation of test cases using natural language processing (NLP), predictive bug detection with anomaly detection models, adaptive testing with reinforcement learning and test suite optimization with previous defect analysis. We provide actual examples of cases used in business to prove the measurable improvement of testing efficiency, test coverage and defect detection rates. The use of ML to test priority and the use of NLP to generate tests automatically has been particularly promising in this area, and has already been demonstrated to have a large potential to save manual effort and improve the quality of tests. But there are still issues, especially when it comes to model interpretability, compatibility with CI/CD pipelines, data privacy and the adjustments of AI models to large API ecosystems. The future direction of how to make AI-enhanced testing more explainable, adaptive, and accessible to development teams of any size is also summarized in this paper
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