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A model independent S/W framework for search-based software testing.

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This study introduces a model-independent framework for Search-Based Software Testing (SBST) to streamline test case generation. The framework significantly reduces redundant work and boosts productivity by approximately 50% when model types change.

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Area of Science:

  • Software Engineering
  • Software Testing
  • Artificial Intelligence in Software Engineering

Background:

  • Model-Based Testing (MBT) utilizes Search-Based Software Testing (SBST) for automated test case generation from system models.
  • Current SBST approaches require reimplementation of search algorithms when model types change, leading to significant time and effort expenditure.
  • This redundancy hinders the efficiency and scalability of SBST in diverse MBT applications.

Purpose of the Study:

  • To propose a novel, model-independent software framework for SBST to address the challenge of model type variability.
  • To reduce redundant development efforts and accelerate the application of SBST techniques across different model types.
  • To demonstrate the effectiveness and efficiency of the proposed framework through empirical case studies.

Main Methods:

  • Development of a reusable, common software platform designed to be independent of specific model types.
  • Integration of design patterns within the framework to facilitate test case generation for various target models.
  • Utilization of common functions provided by the framework to minimize development time and effort.
  • Validation through two distinct case studies comparing the framework's performance against traditional approaches.

Main Results:

  • The proposed model-independent framework successfully reduces redundant work in SBST.
  • The framework provides a reusable platform that significantly decreases development time and effort.
  • Case studies demonstrate a productivity improvement of approximately 50% when switching between different model types.

Conclusions:

  • The developed model-independent SBST framework is effective and efficient for generating test cases across diverse model types.
  • The framework offers a practical solution to overcome the limitations of model-specific SBST implementations.
  • Adoption of this framework can lead to substantial gains in software testing productivity and resource optimization.