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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Formal Methods

Background:

  • Software testers use bug reports to identify abnormal software behavior.
  • Automating the capture of incorrect software behavior is crucial for efficient testing.
  • Finite State Machines (FSMs) can represent software behavior for testing purposes.

Purpose of the Study:

  • To propose a multi-objective evolutionary approach for automatically generating FSMs from natural language bug reports.
  • To enable testers to exercise reported bugs and discover new ones using generated FSMs.
  • To evaluate the effectiveness of different Multi-Objective Evolutionary Algorithms (MOEAs) for this task.

Main Methods:

  • Utilizing a Multi-Objective Evolutionary Algorithm (MOEA) to guide FSM generation.
  • Minimizing three objectives simultaneously: model size, over-generalization, and under-generalization.
  • Assessing the approach on 10 real-world software programs using NSGA-II, NSGA-III, and MOEA/D, benchmarked against KLFA.

Main Results:

  • The baseline tool KLFA is impractical due to over-generalization.
  • NSGA-II significantly outperformed NSGA-III and MOEA/D, detecting more bugs in 90% of tested programs.
  • Using only two objectives resulted in infeasible or suboptimal solutions compared to using three.

Conclusions:

  • The proposed MOEA approach effectively generates FSMs from bug reports for software testing.
  • NSGA-II is the most effective MOEA for this task, balancing model size, accuracy, and coverage.
  • Employing all three objectives (size, over-generalization, under-generalization) leads to diverse, optimized FSMs, avoiding local optima and improving test generation.