Ant Colony Optimization (ACO-Min) Algorithm for Test Suite Minimization

  • Subhasish Mohanty
  • Sudhir Kumar MohapatraEmail author
  • Sultan Feisso Meko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


This paper presents a test suite (TS) minimization algorithm based on ant colony optimization. The algorithm represents all the test cases in the test suite as nodes of a complete graph. Each test case execution time and corresponding test requirement are stored in the form of a matrix. Ants start from the nodes of the complete graph. The selection of neighbor nodes depends on maximizing requirements and minimizing execution time, for which the ant takes the help of the matrix. The algorithm finds a representative set (RS) of test case which satisfies all the requirements and takes least execution time. The derived representative set satisfies (i) |RS| ⊑ |TS| (ii) τRS ≤ τTS. The representative set also preserves the same fault detection capability, like that of the original test suite, which ensures zero compromise on the overall effectiveness of the program.


Regression testing Test case reduction Test case minimization Ant colony optimization Representative set Test suite Test case 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Subhasish Mohanty
    • 1
  • Sudhir Kumar Mohapatra
    • 2
    Email author
  • Sultan Feisso Meko
    • 2
  1. 1.G.I.E.T UniversityGunupurIndia
  2. 2.Addis Ababa Science & Technology UniversityAddis AbabaEthiopia

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