AI-Powered Contract Testing in Microservices: Leveraging OpenAPI, GraphQL, and LSTM-Based Predictive Analysis

Akhil Reddy Bairi (), Prabhu Muthusamy () and Praveen Kumar Dora Mallareddi ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 3, issue 1, 476-492

Abstract: The increasing adoption of microservices architecture necessitates robust testing strategies to ensure seamless integration and communication between services. AI-powered contract testing leverages advanced techniques such as OpenAPI, GraphQL, and Long Short-Term Memory (LSTM)-based predictive analysis to enhance service validation, detect contract violations, and improve test automation. This research explores how AI-driven approaches optimize contract testing by predicting potential integration failures, reducing manual efforts, and improving API reliability. The study highlights key advantages, including automated test generation, real-time anomaly detection, and adaptive testing based on historical API behavior. Furthermore, challenges such as handling evolving contracts, ensuring consistency, and mitigating false positives are discussed. The findings underscore the transformative role of AI in advancing contract testing for microservices, leading to more resilient and efficient software ecosystems.

Keywords: AI-powered contract testing; microservices; OpenAPI; GraphQL; LSTM predictive analysis; API validation (search for similar items in EconPapers)
Date: 2024
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