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Multi-objective regression test suite optimization using three variants of adaptive neuro fuzzy inference system.

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This study introduces novel multi-objective optimization methods for regression testing, significantly reducing test suite size without compromising fault detection. These adaptive neuro-fuzzy inference system variants offer efficient software testing solutions.

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Regression testing is crucial but becomes inefficient due to large, redundant test suites.
  • Existing optimization methods are often single-objective and static, failing to meet dynamic IT demands.
  • Multi-objective dynamic approaches are essential for effective regression test suite optimization.

Purpose of the Study:

  • To propose novel multi-objective dynamic optimization approaches for regression test suites.
  • To introduce three self-tunable Adaptive Neuro-fuzzy Inference System (ANFIS) variants: TLBO-ANFIS, FA-ANFIS, and HS-ANFIS.
  • To address the challenge of reducing regression test suite size while maintaining fault detection capabilities.

Main Methods:

  • Developed three self-tunable Adaptive Neuro-fuzzy Inference System (ANFIS) variants: TLBO-ANFIS, FA-ANFIS, and HS-ANFIS.
  • Utilized two benchmark test suites for evaluating the proposed ANFIS variants.
  • Measured performance using Standard Deviation and Root Mean Square Error.

Main Results:

  • The proposed ANFIS variants effectively reduce the size of regression test suites.
  • The optimization process did not lead to a reduction in the fault detection rate.
  • Experimental results demonstrated the superiority of the proposed methods over existing techniques.

Conclusions:

  • The proposed TLBO-ANFIS, FA-ANFIS, and HS-ANFIS methods provide an effective solution for multi-objective regression test suite optimization.
  • These dynamic, multi-objective approaches are well-suited for modern software development challenges.
  • The research contributes to more efficient and cost-effective software testing practices.