A Novel Fitness function of metaheuristic algorithms for test data generation for simulink models based on mutation analysis

We propose a novel fitness function (FF) to generate test data for Simulink models.Mutation testing is used as a criterion to generate test data for Simulink modelsThe FF is designed by analyzing each mutation operator and the features in Simulink.FF is used in the MPC-GA algorithm to generate test sets for Simulink models.The mutation score has been significantly improved for all models. Testing is one of the crucial activities to assure the software quality. The main objective of testing is to generate test data uncovering faults in software modules. There are a variety of testing techniques in which mutation testing is a popular approach to generate test sets and evaluate their fault detection ability. Simulink is an environment widely used in industry to design and simulate critical systems. Testing such a system at the design phase could help to detect faults earlier. This study aims to propose a novel fitness function of metaheuristic algorithms to generate test data based on the mutation technique for the Simulink models. The fitness function is designed by analyzing each mutation operator and the features of blocks in the Simulink environment in order to guide the search process to reach the test data killing mutants more easily. Then, this fitness function is used in the multi-parent crossover genetic algorithm to generate test sets. The obtained results indicated that the mutation score has been significantly improved for all models when using the novel fitness function. In addition, each stubborn mutant was killed with a lower number of test data evaluations in comparison with the work of other authors.

[1]  Leonardo Bottaci,et al.  Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data , 2003, GECCO.

[2]  Andreas Windisch,et al.  Search-based testing of complex simulink models containing stateflow diagrams , 2009, 2009 31st International Conference on Software Engineering - Companion Volume.

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

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

[5]  John A. Clark,et al.  A search-based framework for automatic testing of MATLAB/Simulink models , 2008, J. Syst. Softw..

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

[7]  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.

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

[9]  Nguyen Thanh Binh,et al.  AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS , 2017 .

[10]  Giuliano Antoniol,et al.  Automatic mutation test input data generation via ant colony , 2007, GECCO '07.

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

[12]  John A. Clark,et al.  The state problem for test generation in Simulink , 2006, GECCO '06.

[13]  Noura Al Moubayed,et al.  Signal Generation for Search-Based Testing of Continuous Systems , 2009, 2009 International Conference on Software Testing, Verification, and Validation Workshops.

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

[15]  Mark Harman.  Automated Test Data Generation using Search Based Software Engineering (keynote) , 2007, ICSE 2007.

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

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

[18]  Daniel Kroening,et al.  Test-case generation for embedded simulink via formal concept analysis , 2011, 2011 48th ACM/EDAC/IEEE Design Automation Conference (DAC).

[19]  Durga Prasad Mohapatra,et al.  Generation of Branch Coverage Test Data for Simulink/Stateflow Models Using Crest Tool , 2013 .

[20]  Ratnesh Kumar,et al.  Model-based automatic test generation for Simulink/Stateflow using extended finite automaton , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

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

[22]  S. Ramesh,et al.  Randomized directed testing (REDIRECT) for Simulink/Stateflow models , 2008, EMSOFT '08.

[23]  Dana Angluin,et al.  Two notions of correctness and their relation to testing , 1982, Acta Informatica.

[24]  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.