An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use

New approach is used to generate combinatorial test suitesFuzzy-based strategy developed to generate the test cases using adaptive techniqueIt is a new approach in developing Artificial Intelligent and Expert systemsThe strategy proves its efficiency, performance compared to its counterpartsThe strategy proves its effectiveness also through the case study Recent research activities have demonstrated the effective application of combinatorial optimization in different areas, especially in software testing. Covering array (CA) has been introduced as a representation of the combinations in one complete set. CAλ(N; t, k, v) is an N?×?k array in which each t-tuple for an N × t sub array occurs at least λ times, where t is the combination strength, k is the number of components (factors), and v is the number of symbols for each component (levels). Generating an optimized covering array for a specific number of k and v is difficult because the problem is a non-deterministic polynomial-time hard computational one. To address this issue, many relevant strategies have been developed, including stochastic population-based algorithms. This paper presents a new and effective approach for constructing efficient covering arrays through fuzzy-based, adaptive particle swarm optimization (PSO). With this approach, efficient covering arrays have been constructed and the performance of PSO has been improved for this type of application. To measure the effectiveness of the technique, an empirical study is conducted on a software system. The technique proves its effectiveness through the conducted case study.

[1]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

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

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

[4]  Bestoun S. Ahmed,et al.  Generating combinatorial test cases using Simplified Swarm Optimization (SSO) algorithm for automated GUI functional testing , 2014 .

[5]  Charles J. Colbourn,et al.  One-test-at-a-time heuristic search for interaction test suites , 2007, GECCO '07.

[6]  Yu Lei,et al.  IPOG-IPOG-D: efficient test generation for multi-way combinatorial testing , 2008 .

[7]  Charles J. Colbourn,et al.  The density algorithm for pairwise interaction testing: Research Articles , 2007 .

[8]  Myra B. Cohen,et al.  Evaluating improvements to a meta-heuristic search for constrained interaction testing , 2011, Empirical Software Engineering.

[9]  Huimin Wang,et al.  Parameter tuning of particle swarm optimization by using Taguchi method and its application to motor design , 2014, 2014 4th IEEE International Conference on Information Science and Technology.

[10]  Tatsuhiro Tsuchiya,et al.  Using artificial life techniques to generate test cases for combinatorial testing , 2004, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004..

[11]  A. Barrett,et al.  A Combinatorial Test Suite Generator for Gray-Box Testing , 2009, 2009 Third IEEE International Conference on Space Mission Challenges for Information Technology.

[12]  Bestoun S. Ahmed,et al.  Using the combinatorial optimization approach for DVS in high performance processors , 2013, 2013 The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE).

[13]  Stefan Lessmann,et al.  Tuning metaheuristics: A data mining based approach for particle swarm optimization , 2011, Expert Syst. Appl..

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

[15]  Charles J. Colbourn,et al.  Merging covering arrays and compressing multiple sequence alignments , 2009, Discret. Appl. Math..

[16]  Z. ZamliKamal,et al.  T-Way Testing Strategies: A Critical Survey and Analysis , 2013 .

[17]  D. Richard Kuhn,et al.  Pseudo-Exhaustive Testing for Software , 2006, 2006 30th Annual IEEE/NASA Software Engineering Workshop.

[18]  Oscar Castillo,et al.  Particle swarm optimization with dynamic parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions , 2013, 2013 World Congress on Nature and Biologically Inspired Computing.

[19]  Kamal Zuhairi Zamli,et al.  A variable strength interaction test suites generation strategy using Particle Swarm Optimization , 2011, J. Syst. Softw..

[20]  Riccardo Poli,et al.  Analysis of the publications on the applications of particle swarm optimisation , 2008 .

[21]  Chee Peng Lim,et al.  Constructing a t-way interaction test suite using the Particle Swarm Optimization approach , 2012 .

[22]  Jun Yan,et al.  Generating combinatorial test suite using combinatorial optimization , 2014, J. Syst. Softw..

[23]  Bestoun S. Ahmed,et al.  Comparison of metahuristic test generation strategies based on interaction elements coverage criterion , 2011, 2011 IEEE Symposium on Industrial Electronics and Applications.

[24]  Baowen Xu,et al.  Greedy Heuristic Algorithms to Generate Variable Strength Combinatorial Test Suite , 2008, 2008 The Eighth International Conference on Quality Software.

[25]  Yu Lei,et al.  In-parameter-order: a test generation strategy for pairwise testing , 1998, Proceedings Third IEEE International High-Assurance Systems Engineering Symposium (Cat. No.98EX231).

[26]  Boris Beizer,et al.  Software Testing Techniques , 1983 .

[27]  Gerard J. Holzmann,et al.  The Model Checker SPIN , 1997, IEEE Trans. Software Eng..

[28]  Myra B. Cohen,et al.  Combinatorial Interaction Regression Testing: A Study of Test Case Generation and Prioritization , 2007, 2007 IEEE International Conference on Software Maintenance.

[29]  Jing Zhang,et al.  Distributed event-triggered control of multiagent systems with general linear dynamics , 2014 .

[30]  Oscar Castillo,et al.  A Comparative Study of Membership Functions for an Interval Type-2 Fuzzy System used to Dynamic Parameter Adaptation in Particle Swarm Optimization , 2014, Recent Advances on Hybrid Approaches for Designing Intelligent Systems.

[31]  Myra B. Cohen,et al.  A framework of greedy methods for constructing interaction test suites , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[32]  Jacek M. Zurada,et al.  An approach to multimodal biomedical image registration utilizing particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[33]  Kari J. Nurmela,et al.  Upper bounds for covering arrays by tabu search , 2004, Discret. Appl. Math..

[34]  Ching-Shui Cheng,et al.  Orthogonal Arrays with Variable Numbers of Symbols , 1980 .

[35]  Myra B. Cohen,et al.  Moving Forward with Combinatorial Interaction Testing , 2014, Computer.

[36]  Boris Beizer,et al.  Software testing techniques (2. ed.) , 1990 .

[37]  Narayana Prasad Padhy,et al.  Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design , 2007 .

[38]  J. Czerwonka.  Pairwise Testing in Real World Practical Extensions to Test Case Generators , 2006 .

[39]  Bestoun S. Ahmed,et al.  Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo Search algorithm , 2015, Inf. Softw. Technol..

[40]  Antonino Staiano,et al.  A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference , 2015, Expert Syst. Appl..

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

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

[43]  Yu Lei,et al.  Refining the In-Parameter-Order Strategy for Constructing Covering Arrays , 2008, Journal of research of the National Institute of Standards and Technology.

[44]  Jeff Yu Lei,et al.  IPOG: A General Strategy for T-Way Software Testing , 2007, 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS'07).

[45]  Xiang Chen,et al.  Applying Particle Swarm Optimization to Pairwise Testing , 2010, 2010 IEEE 34th Annual Computer Software and Applications Conference.

[46]  Kamal Z. Zamli,et al.  A Test Generation Strategy For Variable-Strength and T-way Interaction , 2014 .

[47]  Oscar Castillo,et al.  Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic , 2013, Expert Syst. Appl..

[48]  Pat Langley,et al.  Artificial Intelligence and Intelligent Systems , 2006 .

[49]  Riccardo Poli,et al.  Particle Swarms: The Second Decade , 2008 .

[50]  Myra B. Cohen,et al.  GUI Interaction Testing: Incorporating Event Context , 2011, IEEE Transactions on Software Engineering.

[51]  Alan Hartman,et al.  Problems and algorithms for covering arrays , 2004, Discret. Math..

[52]  Charles J. Colbourn,et al.  The density algorithm for pairwise interaction testing , 2007, Softw. Test. Verification Reliab..

[53]  Bassem Jarboui,et al.  Combinatorial particle swarm optimization (CPSO) for partitional clustering problem , 2007, Appl. Math. Comput..

[54]  Oscar Castillo,et al.  A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation , 2014, Expert Syst. Appl..

[55]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[56]  Angelo Gargantini,et al.  IPO-s: Incremental Generation of Combinatorial Interaction Test Data Based on Symmetries of Covering Arrays , 2009, 2009 International Conference on Software Testing, Verification, and Validation Workshops.

[57]  Ashish Jain,et al.  Modeling requirements for combinatorial software testing , 2005, ACM SIGSOFT Softw. Eng. Notes.

[58]  Myra B. Cohen,et al.  Interaction testing of highly-configurable systems in the presence of constraints , 2007, ISSTA '07.

[59]  Hareton K. N. Leung,et al.  Combinatorial testing, random testing, and adaptive random testing for detecting interaction triggered failures , 2015, Inf. Softw. Technol..

[60]  Oscar Castillo,et al.  Fuzzy Logic for Parameter Tuning in Evolutionary Computation and Bio-inspired Methods , 2010, MICAI.

[61]  Atif M. Memon,et al.  Generating Event Sequence-Based Test Cases Using GUI Runtime State Feedback , 2010, IEEE Transactions on Software Engineering.

[62]  Bestoun S. Ahmed,et al.  Application of combinatorial interaction design for DC servomotor PID controller tuning , 2014 .

[63]  Gang Xu,et al.  An adaptive parameter tuning of particle swarm optimization algorithm , 2013, Appl. Math. Comput..

[64]  Joachim Wegener,et al.  Applying particle swarm optimization to software testing , 2007, GECCO '07.

[65]  Oscar Cordón,et al.  International Journal of Approximate Reasoning a Historical Review of Evolutionary Learning Methods for Mamdani-type Fuzzy Rule-based Systems: Designing Interpretable Genetic Fuzzy Systems , 2022 .

[66]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.