AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS

Mutation testing is a powerful and e ective software testing technique to assess the quality of test suites. Although many research works have been done in the eld of search-based testing, automatic test data generation based on the mutation analysis method is not straightforward. In this paper, an Improved Genetic Algorithm (IGA) is proposed to increase the quality of test data based on mutation coverage criterion. This algorithm involves some modi cations of genetic operators and the employment of memory mechanism to enhance its e ectiveness. The proposed approach is implemented to generate test data for Simulink models. The obtained results indicated that IGA outperformed the conventional genetic algorithm in terms of the quality of test sets, and the execution time.

[1]  Binh Nguyen Thanh,et al.  Survey on Mutation-based Test Data Generation , 2015 .

[2]  Yuan Zhan,et al.  A search-based framework for automatic test-set generation for MATLAB/Simulink models , 2005 .

[3]  Phil McMinn,et al.  Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..

[4]  A Jeeerson Ooutt,et al.  Subsumption of Condition Coverage Techniques by Mutation Testing , 1996 .

[5]  Claes Wohlin,et al.  Experimentation in software engineering: an introduction , 2000 .

[6]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[7]  I. K. Mak,et al.  Adaptive Random Testing , 2004, ASIAN.

[8]  Xiong Xu,et al.  An adaptive fitness function based on branch hardness for search based testing , 2017, GECCO.

[9]  Marc Roper,et al.  Genetic Algorithms and the Automatic Generation of Test Data , 2007 .

[10]  John A. Clark,et al.  Comparing algorithms for search-based test data generation of Matlab® Simulink® models , 2009, 2009 IEEE Congress on Evolutionary Computation.

[11]  Baowen Xu,et al.  Application of Genetic Algorithms in Software Testing , 2007 .

[12]  M. Masud,et al.  Strategy for mutation testing using genetic algorithms , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[13]  Florentin Ipate,et al.  Automatic State-Based Test Generation Using Genetic Algorithms , 2007, Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007).

[14]  Peter S. May,et al.  Test data generation : two evolutionary approaches to mutation testing , 2007 .

[15]  Nikolai Tillmann,et al.  Test generation via Dynamic Symbolic Execution for mutation testing , 2010, 2010 IEEE International Conference on Software Maintenance.

[16]  Mark Harman,et al.  A study of equivalent and stubborn mutation operators using human analysis of equivalence , 2014, ICSE.

[17]  Mike Papadakis,et al.  Automatic Mutation Test Case Generation via Dynamic Symbolic Execution , 2010, 2010 IEEE 21st International Symposium on Software Reliability Engineering.

[18]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[19]  Richard J. Lipton,et al.  Hints on Test Data Selection: Help for the Practicing Programmer , 1978, Computer.

[20]  Abraham Kandel,et al.  Effective Black-Box Testing with Genetic Algorithms , 2005, Haifa Verification Conference.

[21]  H. Haga,et al.  Automatic test case generation based on genetic algorithm and mutation analysis , 2012, 2012 IEEE International Conference on Control System, Computing and Engineering.

[22]  Moheb R. Girgis,et al.  Automatic Test Data Generation for Data Flow Testing Using a Genetic Algorithm , 2005, J. Univers. Comput. Sci..

[23]  Roy P. Pargas,et al.  Mutation Testing of Software Using MIMD Computer , 1992, ICPP.

[24]  Thanh Binh Nguyen,et al.  Mutation Operators for Simulink Models , 2012, 2012 Fourth International Conference on Knowledge and Systems Engineering.

[25]  Mark Harman,et al.  Transition coverage testing for simulink/stateflow models using messy genetic algorithms , 2011, GECCO '11.

[26]  Robert M. Hierons,et al.  Aiding Test Case Generation in Temporally Constrained State Based Systems Using Genetic Algorithms , 2009, IWANN.

[27]  Yaduvir Singh,et al.  Genetic Algorithms: Concepts, Design for Optimization of Process Controllers , 2011, Comput. Inf. Sci..

[28]  Cássio L. Rodrigues,et al.  An elitist evolutionary algorithm for automatically generating test data , 2012, 2012 IEEE Congress on Evolutionary Computation.

[29]  Thanh Binh Nguyen,et al.  Applying the meta-heuristic algorithms for mutation-based test data generation for Simulink models , 2014, SoICT.

[30]  Jean-Marc Jézéquel,et al.  Genes and bacteria for automatic test cases optimization in the .NET environment , 2002, 13th International Symposium on Software Reliability Engineering, 2002. Proceedings..

[31]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[32]  Simone do Rocio Senger de Souza,et al.  The complementary aspect of automatically and manually generated test case sets , 2016, A-TEST@SIGSOFT FSE.

[33]  Ciprian Paduraru,et al.  A distributed implementation using apache spark of a genetic algorithm applied to test data generation , 2017, GECCO.

[34]  Lionel C. Briand,et al.  A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation , 2010, IEEE Transactions on Software Engineering.

[35]  Santanu Kumar Rath,et al.  A Genetic Algorithm based Approach for Test Data Generation in Basis Path Testing , 2014, ArXiv.

[36]  Thanh Binh Nguyen,et al.  A Novel Test Data Generation Approach Based Upon Mutation Testing by Using Artificial Immune System for Simulink Models , 2014, KSE.

[37]  Leonardo Bottaci,et al.  A Genetic Algorithm Fitness Function for Mutation Testing , 2001 .

[38]  Andreas Windisch.  Search-based test data generation from stateflow statecharts , 2010, GECCO '10.

[39]  Khuat Thanh Tung,et al.  Mutation-based Test Data Generation for Simulink Models using Genetic Algorithm and Simulated Annealing , 2014 .

[40]  A. Jefferson Offutt,et al.  Constraint-Based Automatic Test Data Generation , 1991, IEEE Trans. Software Eng..

[41]  Mike Papadakis,et al.  Strong Mutation-Based Test Data Generation Using Hill Climbing , 2016, 2016 IEEE/ACM 9th International Workshop on Search-Based Software Testing (SBST).

[42]  Lu Lu,et al.  A new approach for session-based test case generation by GA , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[43]  Inmaculada Medina-Bulo,et al.  Mutation Testing: Guideline and Mutation Operator Classification , 2014 .

[44]  Yogesh Singh.  Automated Expected Output Generation: Is this a Problem that has been solved? , 2015, SOEN.