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<p>Service-oriented computing enables service reuse across different contexts, with applications heavily relying on third-party lightweight HTTP services. Testing such applications, however, presents significant challenges when actual services are inaccessible. Service virtualisation provides a technique to overcome dependency issues by simulating service behaviour through synthetic responses. This paper investigates the suitability of Description Logic learning as a symbolic machine learning technique for automatically constructing HTTP service mock skeletons from network traffic recordings. Experiments demonstrate strong predictive performance with human-readable logical expressions for key response attributes including status codes and headers, capturing both protocol semantics and temporal dependencies on prior transaction sequences. The resulting mock skeletons facilitate comprehension of the logical reasoning behind response properties and can be customised to create mocks that generate responses suitable for testing.</p>

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Generating Mock Skeletons for Lightweight Web-Service Testing with Description Logic Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

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  • Thilini Bhagya,
  • Jens Dietrich,
  • Hans Guesgen

摘要

Service-oriented computing enables service reuse across different contexts, with applications heavily relying on third-party lightweight HTTP services. Testing such applications, however, presents significant challenges when actual services are inaccessible. Service virtualisation provides a technique to overcome dependency issues by simulating service behaviour through synthetic responses. This paper investigates the suitability of Description Logic learning as a symbolic machine learning technique for automatically constructing HTTP service mock skeletons from network traffic recordings. Experiments demonstrate strong predictive performance with human-readable logical expressions for key response attributes including status codes and headers, capturing both protocol semantics and temporal dependencies on prior transaction sequences. The resulting mock skeletons facilitate comprehension of the logical reasoning behind response properties and can be customised to create mocks that generate responses suitable for testing.