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

Author

Listed:
  • Akhil Reddy Bairi
  • Prabhu Muthusamy
  • Praveen Kumar Dora Mallareddi

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.

Suggested Citation

  • Akhil Reddy Bairi & Prabhu Muthusamy & Praveen Kumar Dora Mallareddi, 2024. "AI-Powered Contract Testing in Microservices: Leveraging OpenAPI, GraphQL, and LSTM-Based Predictive Analysis," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 3(1), pages 476-492.
    • Handle: RePEc:das:njaigs:v:3:y:2024:i:1:p:476-492:id:339
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