Open AccessAdopting Ant Colony Optimization Algorithm for Pairwise T-Way Test Suite Generation Strategy
Author(s) -
Norazan Mohamed Ramli,
Rozmie Razif Othman,
Rimuljo Hendradi,
Shukor Sanim Mohd Fauzi,
I. Ismail,
Muhammad Munsif Ahmad,
Mohd Wafi Nasrudin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1962/1/012045
Subject(s) - test suite , pairwise comparison , benchmark (surveying) , computer science , suite , ant colony optimization algorithms , algorithm , test case , mathematical optimization , artificial intelligence , machine learning , mathematics , regression analysis , archaeology , geography , geodesy , history
Combinatorial testing or t-way testing (t represents strength) is useful to detect faults due to interactions. Pairwise testing is one type of t-way testing. The technique is effective in reducing the number of test cases without decreasing the level of coverage. Besides, its purpose is to overcome the problem of exhaustive testing that produces a great number of test cases and is impossible to be implemented due to time and cost constraints. Pairwise T-way Test Suite Generation Strategy based on Ant Colony Optimization (pTTSGA) is introduced to generate a near-optimum test suite size. Experiments have been conducted to evaluate the ability of this strategy for pairwise testing. The results are compared to benchmark results. Overall, pTTSGA produces a comparable test suite size.