Hybrid Artificial Bee Colony Algorithm for t-Way Interaction Test Suite Generation

The very large number of test cases and time consumption for a test, it is becoming hard to perform exhaustive testing for any software fault detection. For this reason, combinatorial testing (CT) also known as t-way testing, is one of the well-known methods that are used for fault detections to many software systems. Various existing research works are available in the literature to minimize the number of test cases, and the time to obtain an optimal test suite or competitive test suite. However, the interaction strength of the existing research works are supports up to t = 2 or t = 3, where t is the strength of parameter’s interaction. The major purpose of this research is to suggest a new t-way strategy to minimize the test cases. This is called hybrid artificial bee colony (HABC) strategy, which is based on hybridize of an artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. This is to provide a high-interaction strength combinatorial test suite up to t = 6. From experimental results, HABC strategy performed best when compared with existing methods in terms of generating the optimum test case.

[1]  Hareton K. N. Leung,et al.  A survey of combinatorial testing , 2011, CSUR.

[2]  Bestoun S. Ahmed,et al.  Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites , 2017, Eng. Appl. Artif. Intell..

[3]  Myra B. Cohen,et al.  Designing Test Suites for Software Interactions Testing , 2004 .

[4]  Abdul Majid Mazlina,et al.  A Bat-inspired Strategy for Pairwise Testing , 2015 .

[5]  Z. ZamliKamal,et al.  Benchmarking of Bat-inspired Interaction Testing Strategy , 2016 .

[6]  Kamal Z. Zamli,et al.  SPLBA: An interaction strategy for testing software product lines using the Bat-inspired algorithm , 2015, 2015 4th International Conference on Software Engineering and Computer Systems (ICSECS).

[7]  Myra B. Cohen,et al.  Constructing strength three covering arrays with augmented annealing , 2003, Discret. Math..

[8]  Ammar K. Alazzawi,et al.  Artificial Bee Colony Algorithm for Pairwise Test Generation , 2017 .

[9]  Xiaohui Yan,et al.  A Hybrid Artificial Bee Colony algorithm for numerical function optimization , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[10]  Graham Kendall,et al.  An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation , 2017, Inf. Sci..

[11]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[12]  Kamal Z. Zamli,et al.  Design and implementation of a harmony-search-based variable-strength t-way testing strategy with constraints support , 2012, Inf. Softw. Technol..

[13]  Xiang Chen,et al.  Variable Strength Interaction Testing with an Ant Colony System Approach , 2009, 2009 16th Asia-Pacific Software Engineering Conference.

[14]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[15]  Kamal Z. Zamli,et al.  Test Cases Minimization Strategy Based on Flower Pollination Algorithm , 2017 .

[16]  Kamal Z. Zamli,et al.  PSTG: A T-Way Strategy Adopting Particle Swarm Optimization , 2010, 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation.

[17]  Basem Y. Alkazemi,et al.  Comparative Benchmarking Of Constraints T-Way Test Generation Strategy Based On Late Acceptance Hill Climbing Algorithm , 2015 .

[18]  Shuib Basri,et al.  Artificial Bee Colony Algorithm for t-Way Test Suite Generation , 2018, 2018 4th International Conference on Computer and Information Sciences (ICCOINS).

[19]  Shouda Jiang,et al.  Constraint Test Cases Generation Based on Particle Swarm Optimization , 2017 .

[20]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[21]  Vahid Rafe,et al.  A tuned version of genetic algorithm for efficient test suite generation in interactive t-way testing strategy , 2018, Inf. Softw. Technol..

[22]  Kamal Z. Zamli,et al.  Adapting the elitism on greedy algorithm for variable strength combinatorial test cases generation , 2019, IET Softw..

[23]  Z. ZamliKamal,et al.  A Bat-inspired Strategy for T-Way Interaction Testing , 2015 .

[24]  Md. Rafiqul Islam,et al.  A Novel Swarm Intelligence Based Strategy to Generate Optimum Test Data in T-Way Testing , 2017 .

[25]  D.M. Cohen,et al.  The Combinatorial Design Approach to Automatic Test Generation , 1996, IEEE Softw..

[26]  Chee Peng Lim,et al.  Application of Particle Swarm Optimization to uniform and variable strength covering array construction , 2012, Appl. Soft Comput..

[27]  Yang Zhang,et al.  Variable Strength Combinatorial Test Data Generation Using Enhanced Bird Swarm Algorithm , 2018, 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).