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Hybrid optimization with constraints handling for combinatorial test case prioritization problems.

Selvakumar J1, Sudhir Sharma2, Mukesh Kumar Tripathi3

  • 1Department of Computer Science & Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.

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|July 11, 2025
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Summary
This summary is machine-generated.

This study introduces a new optimization algorithm for test case prioritization (TCP) in software development. The Fractional Hybrid Leader Based Optimization (FHLO) effectively prioritizes test cases to detect faults earlier and reduce costs.

Keywords:
Fractional calculusconstraintshybrid leader based optimizationtest case prioritization

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Area of Science:

  • Software Engineering
  • Computer Science
  • Optimization Algorithms

Background:

  • Software testing is critical for ensuring software quality and effectiveness.
  • Test case prioritization (TCP) aims to minimize execution time for test suites.
  • Existing TCP research often focuses on time and fault constraints.

Purpose of the Study:

  • To introduce a novel optimization algorithm for combinatorial TCP with constraint handling.
  • To improve early fault detection and reduce regression testing costs.
  • To prioritize test cases based on fault detection and branch coverage.

Main Methods:

  • Development of the Fractional Hybrid Leader Based Optimization (FHLO) algorithm.
  • FHLO is applied to combinatorial test case prioritization problems.
  • Prioritization relies on maximizing Average Percentage of Branch Coverage (APBC) and Average Percentage of Fault Detected (APFD).

Main Results:

  • The FHLO algorithm achieved a maximum APFD of 0.966.
  • The FHLO algorithm achieved a maximum APBC of 0.888.
  • Demonstrated effectiveness in prioritizing test cases for fault detection.

Conclusions:

  • The FHLO algorithm is an effective technique for test case prioritization.
  • FHLO enhances early fault detection and optimizes test execution.
  • The algorithm provides significant improvements in APFD and APBC metrics.