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Related Concept Videos

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Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm.

Shayma Mustafa Mohi-Aldeen1, Radziah Mohamad2, Safaai Deris2

  • 1College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq.

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Summary
This summary is machine-generated.

This study introduces a hybrid Negative Selection Algorithm-Genetic Algorithm (NSA-GA) for optimal test data generation. The method enhances path coverage in software testing while minimizing redundant test data.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Path testing is crucial for white-box software testing.
  • Increasing software complexity makes exhaustive testing infeasible.
  • Generating optimal test data to maximize path coverage is a significant challenge.

Purpose of the Study:

  • To propose a hybrid method combining Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) for efficient test data generation.
  • To improve path coverage and minimize redundant test data in software testing.
  • To enhance the effectiveness and efficiency of test data generation for complex software.

Main Methods:

  • A hybrid NSA-GA approach was developed, modifying NSA's detector generation with GA.
  • A fitness function was designed based on path prioritization.
  • The method was evaluated on benchmark programs with diverse data types.

Main Results:

  • The hybrid NSA-GA method significantly improved path coverage percentages, including for complex paths.
  • The approach effectively minimized the quantity of generated test data.
  • Enhanced efficiency was observed, even with expanded input ranges and varied data types.

Conclusions:

  • The NSA-GA hybrid method offers an effective and efficient solution for test data generation.
  • This approach maximizes search space utilization, leading to increased path coverage.
  • The method successfully prevents redundant data generation, optimizing the testing process.