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Mining Service Behavior for Stateful Service Emulation

Md Arafat Hossain, Jun Han, Muhammad Ashad Kabir, Steve Versteeg, Jean-Guy Schneider, Jiaojiao Jiang

TL;DR

This work tackles accurate testing of enterprise systems by modeling stateful service behavior from interaction traces to produce realistic virtual services. It introduces a message dependency model and a two-track dependency separation (key payload vs non-key) combined with PTA/kTail-based inference to capture stateful interactions, and a payload-population mechanism guided by message formats and equality rules for runtime responses. Across LDAP, SOAP, and REST datasets, the method achieves higher identical and data-consistent responses than strong baselines while avoiding malformed outputs, demonstrating practical gains in accuracy and efficiency. The approach offers significant implications for scalable, realistic service virtualization, with future work focusing on automatic key-payload discovery, richer payload dynamics, and asynchronous protocol handling.

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.

Mining Service Behavior for Stateful Service Emulation

TL;DR

This work tackles accurate testing of enterprise systems by modeling stateful service behavior from interaction traces to produce realistic virtual services. It introduces a message dependency model and a two-track dependency separation (key payload vs non-key) combined with PTA/kTail-based inference to capture stateful interactions, and a payload-population mechanism guided by message formats and equality rules for runtime responses. Across LDAP, SOAP, and REST datasets, the method achieves higher identical and data-consistent responses than strong baselines while avoiding malformed outputs, demonstrating practical gains in accuracy and efficiency. The approach offers significant implications for scalable, realistic service virtualization, with future work focusing on automatic key-payload discovery, richer payload dynamics, and asynchronous protocol handling.

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.
Paper Structure (26 sections, 7 figures, 5 tables, 3 algorithms)

This paper contains 26 sections, 7 figures, 5 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background and Problem Analysis
  3. Related Work
  4. Traditional Techniques for Creating Testing Environments
  5. Service Virtualization Techniques
  6. Techniques for Inferring System Control Models
  7. Approach
  8. Message Analysis - An Overview
  9. Dependency Analysis
  10. Service Trace Partition
  11. Model Trace Generation
  12. Dependency Model Inference
  13. Dependency Model Separation
  14. Response Generation
  15. Response Type Selection
  16. ...and 11 more sections

Figures (7)

  • Figure 1: Enterprise system in actual and virtualized testing environments
  • Figure 2: Approach overview
  • Figure 3: Prefix tree acceptor (PTA)
  • Figure 4: Inferred message dependency models
  • Figure 5: Inferred probabilistic message dependency model (key payload)
  • ...and 2 more figures