Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for Structural Testing

Software testing is a critical and labor-intensive activity in software engineering. Much research has been done to help automate test case generation. This research proposes a new approach to structural test case generation. It uses a specialized genetic algorithm called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) to find a structurally complete set of test cases for the Triangle Classification algorithm. DSGA is a Niche Genetic Algorithm (NGA) that uses a short-term memory structure to store optima. Each individual of the NGA represents the inputs for a test case. The fitness function encourages the algorithm to locate test cases that cover large areas of the structure of the program. A shared fitness encourages the NGA to locate other areas of the structure. DSGA is a novel approach to structurally complete test case generation.

[1]  Joachim Wegener,et al.  Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System , 2004, GECCO.

[2]  Andreas S. Andreou,et al.  Automatic, evolutionary test data generation for dynamic software testing , 2008, J. Syst. Softw..

[3]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.

[4]  Ashwin Panchapakesan.  A Hybrid Genetic Algorithm and Evolutionary Strategy to Automatically Generate Test Data for Dynamic, White-Box Testing , 2013 .

[5]  Myra B. Cohen,et al.  An orchestrated survey of methodologies for automated software test case generation , 2013, J. Syst. Softw..

[6]  Gary McGraw,et al.  Generating Software Test Data by Evolution , 2001, IEEE Trans. Software Eng..

[7]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[8]  Tianlong Man,et al.  Improved Ant Algorithms for Software Testing Cases Generation , 2014, TheScientificWorldJournal.

[9]  Peter Grogono,et al.  Using a genetic algorithm and formal concept analysis to generate branch coverage test data automatically , 2004, Proceedings. 19th International Conference on Automated Software Engineering, 2004..

[10]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[11]  Vinicius H. S. Durelli,et al.  Toward harnessing a Java high-level language virtual machine for supporting software testing , 2013 .

[12]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[13]  A. Jefferson Offutt,et al.  Toward Harnessing High-Level Language Virtual Machines for Further Speeding Up Weak Mutation Testing , 2012, 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation.

[14]  A. Jefferson Offutt,et al.  Introduction to Software Testing , 2008 .

[15]  S. Nachiyappan,et al.  An evolutionary algorithm for regression test suite reduction , 2010, 2010 International Conference on Communication and Computational Intelligence (INCOCCI).

[16]  P. John Clarkson,et al.  A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2002, Evolutionary Computation.

[17]  Michael Scott.  A Species-Conserving Genetic Algorithm for Multimodal Optimization , 2010 .

[18]  Mukesh Kumar Rohil,et al.  Using Genetic Algorithm for Unit Testing of Object Oriented Software , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[19]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

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

[21]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[22]  Michael J. Pelosi,et al.  Dynamic-Radius Species-Conserving Genetic Algorithm for the Financial Forecasting of Dow Jones Index Stocks , 2013, MLDM.

[23]  Name M. Lastname.  Automatically Finding Patches Using Genetic Programming , 2013 .

[24]  James A. Coker,et al.  Niche Genetic Algorithms are better than traditional Genetic Algorithms for de novo Protein Folding , 2014, F1000Research.

[25]  Mauro Pezzè,et al.  Software testing and analysis - process, principles and techniques , 2007 .

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