Intelligent API Contract Testing Using Large Language Models and Machine Learning Techniques

Abstract

Intelligent API Contract Testing has emerged as a vital tool for software quality improvement in cloud-native microservice architectures. However, traditional methods for API contract testing validate only schemas of requests and responses without discovering semantic inconsistencies, behavioural drifts, and changes in API interaction. This paper explores how Large Language Models (LLMs) and Machine Learning (ML) enhance the process of API contract testing by means of automatic validation, reasoning, anomaly detection, and test generation. The study has been carried out via secondary data collection by reviewing the existing literature based on peer-reviewed journal papers, technical reports, industry materials, and the Stanford Artificial Intelligence Index Report 2021. Qualitative research was chosen to interpret the available evidence, and an inductive approach for developing the findings on the basis of the existing research. The thematic analysis was used as a tool for identifying themes associated with the performance of LLMs, accuracy of machine learning, API contract error detection, software quality, and cloud-native testing. The results indicate that API validation through machine learning provides accuracy of 79% to 92.1% in contract violation prediction and semantic consistency detection. Hybrid approaches using both LLMs and machine learning (ML) enhance the accuracy of the validation process, minimise false alerts and provide support. The paper also draws attention to the fact that AI is becoming increasingly involved in software engineering, which is evident from the number of publications and cooperation with the industry. As a result, intelligent API contract testing can be considered an automated approach that guarantees API compatibility and software quality.