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Reliability of APIs plays a vital role in the stability of distributed systems and DevOps workflows. Although chaos engineering has proven effective in exposing vulnerabilities, its integration into API testing remains limited due to a lack of automation and efficient test case generation. This paper introduces ChaosAPI, a novel tool that employs genetic algorithms to automate chaos engineering specifically for API testing. By utilizing OpenAPI Specifications, ChaosAPI systematically generates, mutates, and optimizes test cases to improve coverage and uncover hard-to-detect edge cases. Comparative analysis shows that ChaosAPI performs competitively with state-of-the-art methods, including NLP-based testing techniques, Llama 3 8B, and JetBrains AI Assistant—despite requiring less computational power and not relying on access to source code.

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Enhancing API Testing Through Genetic Algorithm-Based Chaos Engineering

  • 会议论文

  • 发表日期：

    2026年

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  • Mehdi Ait Said,
  • Lahcen Belouaddane,
  • Mihi Soukaina,
  • Ezzati Abdellah,
  • Zraibi Brahim

摘要

Reliability of APIs plays a vital role in the stability of distributed systems and DevOps workflows. Although chaos engineering has proven effective in exposing vulnerabilities, its integration into API testing remains limited due to a lack of automation and efficient test case generation. This paper introduces ChaosAPI, a novel tool that employs genetic algorithms to automate chaos engineering specifically for API testing. By utilizing OpenAPI Specifications, ChaosAPI systematically generates, mutates, and optimizes test cases to improve coverage and uncover hard-to-detect edge cases. Comparative analysis shows that ChaosAPI performs competitively with state-of-the-art methods, including NLP-based testing techniques, Llama 3 8B, and JetBrains AI Assistant—despite requiring less computational power and not relying on access to source code.