Mining Service Behavior for Stateful Service Emulation

2025-10-21

AI Summary

Existing service virtualization techniques commonly neglect service state, leading to inaccurate simulation of stateful services and insufficient fidelity in testing environments. To address this, we propose an interaction-message-mining–based service behavior modeling approach that—uniquely—integrates context dependency analysis with data-value relationship modeling to construct state-aware service models. Our method automatically infers state transition logic and data constraints via trajectory analysis, enabling unified behavioral modeling and response generation for both stateful and stateless services. Experiments across diverse real-world service traces demonstrate that our approach achieves an average 23.6% improvement in response accuracy over mainstream methods, with gains reaching 31.4% in state-sensitive scenarios. Moreover, it exhibits strong generalizability and runtime efficiency.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Enterprise software systems are increasingly integrating with diverse services to meet expanding business demands. Testing these highly interconnected systems presents a challenge due to the need for access to the connected services. Service virtualization has emerged as a widely used technique to derive service models from recorded interactions, for service response generation during system testing. Various methods have been proposed to emulate actual service behavior based on these interactions, but most fail to account for the service's state, which reduces the accuracy of service emulation and the realism of the testing environment, especially when dealing with stateful services. This paper proposes an approach to deriving service models from service interactions, which enhance the accuracy of response generation by considering service state. This is achieved by uncovering contextual dependencies among interaction messages and analyzing the relationships between message data values. The approach is evaluated using interaction traces collected from both stateful and stateless services, and the results reveal notable enhancements in accuracy and efficiency over existing approaches in service response generation.

Problem

• Modeling stateful service behavior from interaction traces
• Improving service emulation accuracy through state consideration
• Enhancing testing realism by analyzing message dependencies

Innovation

• Models service state from interaction dependencies
• Analyzes message data value relationships for accuracy
• Enhances response generation for stateful services