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Artificial Intelligence Review

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27.08.2016

Evolution or revolution: the critical need in genetic algorithm based testing

verfasst von: Anupama Surendran, Philip Samuel

Erschienen in: Artificial Intelligence Review | Ausgabe 3/2017

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Abstract

Software testing is one of the most inevitable processes in software development. The field of software testing has seen an extensive use of search based techniques in the last decade. Among the search based techniques, it is the metaheuristic techniques such as genetic algorithm that has garnered the major share of attention from researchers. Looking at the large body of work that has happened and is happening in this field, we feel that it is high time someone studied how well genetic algorithm based techniques fare in practical testing process. Method: In this work, we present a roadmap to the future of genetic algorithm based software testing, based on a review of literature. We have mainly reviewed the works which use genetic algorithm for software test data generation. This independent review is designed to direct the attention of future researchers to the deficiencies of genetic algorithm based testing, their possible solutions and the extent to which they are correctable. The observations from the selected primary studies highlight the issues faced when genetic algorithm is applied in software testing. The observations form the review reveal that the type of genetic algorithm used, fitness function design, population initialization and parameter settings does impact the quality of solution obtained in software testing using genetic algorithm. From the review we conclude that, more generalized approaches can make genetic algorithm based software testing one of the strongest methods in practical software testing. We hope that, this review will be a major breakthrough in genetic algorithm based software testing field.

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Ahmed MA, Hermadi I (2008) GA-based multiple paths test data generator. Comp Opera Res 35:3107–3127CrossRef

Ali S, Briand LC, Hemmati H, Panesar-Walawege RK (2009) A systematic review of the application and empirical investigation of search-based test case generation. In: IEEE Transactions on Software Engineering, vol. 99

Ali S, Iqbal MZ, Arcuri A, Briand L (2011) A search-based OCL constraint solver for model-based test data generation. In: Proceedings of the 11th international conference on quality software, pp. 41–50

Alshahwan N, Harman M (2014) Coverage and fault detection of the output-uniqueness test selection criteria. In: Proc. ISSTA, pp. 1–12

Arcuri A, Fraser G (2011) On parameter tuning in search based software engineering. In: Proc. SSBSE, pp. 33–47

Arcuri A, Fraser G (2013) Parameter tuning or default values? An empirical investigation in search-based software engineering. Empir Softw Eng 18(3):594–623CrossRef

Beizer B (1990) Software testing technique. Van Nostrand Rheinhold, New YorkMATH

Berndt DJ, Watkins A (2004) Investigating the performance of genetic algorithm-based software test case generation. In: Proc. HASE

Binkley D, Harman M, Lakhotia K (2009) FlagRemover: a testability transformation for transforming loop assigned Flag. ACM Trans Softw Eng Methodol 2(3):110–146

Bueno PM, Jino S (2002) Automatic test data generation for program paths using genetic algorithms. Int J Softw Eng Knowl Eng 12(6):691–709CrossRef

Campos J, Arcuri A, Fraser G, Abreu R (2014) Continuous test generation: enhancing continuous integration with automated test generation. In: Proc. Automated Software Engineering (ASE)

Cao Y, Hu C, Li L (2009) An approach to generate software test data for a specific path automatically with genetic algorithm. In: Proc. ICRMS, Chengdu, pp. 888–892

Chen C, Xu X, Chen Y, Li X, Guo D (2009) A new method of test data generation for branch coverage in software testing based on EPDG and genetic algorithm. In: Proc. ASID, pp. 307–310

Doungsa-ard C, Dahal K, Hossain A, Suwannasart T (2007) Test data generation from UML state machine diagrams using gas. In: Proc. ICSEA

Feldt R, Poulding S (2015) Broadening the search in search-based software testing: it need not be evolutionary. In: Proc. Eighth international workshop on search-based software testing, pp. 1–7

Fischer M (2012) Generating test data for black-box testing using Genetic algorithms. In: Proc. 17th ETFA, pp. 1–6

Fraser G, Arcuri A, McMinn P (2015) A memetic algorithm for whole test suite generation. J Syst Softw 103:311–327. doi:10.1016/j.jss.2014.05.032 CrossRef

Fraser G, Arcuri A (2011a) EvoSuite: automatic test suite generation for object-oriented software. In: Proc. ESEC/FSE, Szeged, Hungary, pp. 416–419

Fraser G, Arcuri A (2011b) It is not the length that matters, it is how you control it. In: Proc. IEEE international conference on software testing, verification and validation, pp. 150–159

Fraser G, Arcuri A (2012) The seed is strong: seeding strategies in search-based software testing. In: Proc. ICST, pp. 121–130

Fraser G, Arcuri A (2013) Whole test suite generation. IEEE Trans Softw Eng 39(2):276–291CrossRef

Fraser G, Arcuri A, McMinn P (2013) Test suite generation with memetic algorithms. In: Proc. GECCO

Galeotti JP, Fraser G, Arcuri A (2014) Extending a search-based test generator with adaptive dynamic symbolic execution. In: Proc. ISSTA

Girgis MR (2005) Automatic test data generation for data flow testing using a genetic algorithm. J Univ Comp Sci 11(5):898–915

Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, BostonMATH

Gonga D, Zhanga W, Yaob X (2011) Evolutionary generation of test data for many paths coverage based on grouping. J Syst Softw 84:2222–2233CrossRef

Graham-Rowe D (2002) Radio emerges from the electronic soup. New Sci 175(2358):19

Harman M (2007) The current state and future of search based software engineering. In: Proc. FOSE, pp. 342–357

Harman M, Mansouri SA, Zhang Y (2012) Search-based software engineering: trends, techniques and applications. ACM Comp Surv 45(1):1–66CrossRef

Harman M, Jia Y, Zhang Y (2015) Achievements, open problems and challenges for search based software testing. In: Proc. 8th IEEE international conference on software testing, verification and validation

Harman M, McMinn P (2010) A theoretical and empirical study of search based testing: local, global and hybrid search. IEEE Trans Softw Eng 36(2):226–247CrossRef

Harman M, McMinn P, Wegener J (2007) The impact of input domain reduction on search based test data generation. In: Proc. ESEC/FSE, Croatia, pp. 155–164

Hermadi I, Lokan C, Sarker R (2014) Dynamic stopping criteria for search-based test data generation for path testing. Inf Softw Technol J 56(4):395–407CrossRef

Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

Jones B, Sthamer H, Eyres D (1996) Automatic structural testing using genetic algorithms. Softw Eng J 11(5):299–306CrossRef

Jorgensen PC (2008) Software testing: a Craftsman’s approach. Auerbach Publications, Boca Raton

Khor S, Grogono P (2004) Using a genetic algorithm and formal concept analysis to generate branch coverage test data automatically. In: Proc. ASE

Kitchenham BA (2007) Guidelines for performing systematic literature reviews in software engineering. Technical report EBSE- 2007-01

Korel B (1990) Automated software test data generation. IEEE Trans Softw Eng 16(8):870–879CrossRef

Latiu GI, Cret OA, Vacariu L (2012) Automatic test data generation for software path testing using evolutionary algorithms. In: Proc. Third international conference on emerging intelligent data and web technologies, pp.1–8

Li J, Baob W, Zhaoa Y, Maa Z, Donga H (2009) Evolutionary generation of unique input/output sequences for class behavioral testing. Comput Math Appl 57:1800–1807CrossRef

Lin P, Bao XL, Shu ZY, Wang XJ, Liu JM (2012) Test case generation based on adaptive genetic algorithm. In: International conference on quality, reliability, risk, maintenance, and safety engineering, Chengdu, China, pp. 863–866

Liu D, Wang X, Wang J (2013) Automatic test case generation based on genetic algorithm. J Theor Appl Inf Technol 48(1):411–416

Louzada J, Camilo-Junior CG, Vincenzi A, Rodrigues C (2012) An elitist evolutionary algorithm for automatically generating test data. In: Proc. IEEE WCCI, Brisbane, Australia

Malburg J, Fraser G (2011) Combining search based and constraint-based testing. In: Proc. IEEE ASE, Lawrence, KS, pp. 436–439

Mantere T, Alander JT (2005) Evolutionary software engineering, A review. J Appl Soft Comput 5:315–333CrossRef

Mao C, Yu X (2013) Test data generation for software testing based on quantum-inspired genetic algorithm. Int J Comput Intell Appl 12(1) pp. 1350004 (21 pages)

McMinn P (2004) Search-based software test data generation: a survey. Softw Test Verif Reliab 14(2):105–156

McMinn P (2005) Evolutionary search for test data in the presence of state behavior. Ph. D. Thesis, University of Sheffield, Sheffield

McMinn P (2011) Search-based software testing: past, present and future. In: Proc. 4th international conference on software testing, verification and validation workshops, pp. 153–163

McMinn P (2013) An identification of program factors that impact crossover performance in evolutionary test input generation for the branch coverage of C programs. Inf Softw Technol 55:153–172CrossRef

Michael CC, McGraw GE, Schatz MA (2001) Generating software test data by evolution. IEEE Trans Softw Eng 27(12):1085–1110CrossRef

Miller J, Reformat M, Zhang H (2006) Automatic test data generation using genetic algorithm and program dependence graphs. Inf Softw Technol 48:586–605CrossRef

Myers G (1979) The art of software testing. Wiley, New York

Oxman AD (1994) Systematic reviews: checklists for review articles. BMJ 309(6955):648–651CrossRef

Pachauri A, Gursaran (2011) Software test data generation using path prefix strategy and genetic algorithm. In: Proc. international conference on science and engineering, pp. 131–140

Pargas RP, Harrold MJ, Peck RR (1999) Test data generation using genetic algorithms. J Softw Test Verif Reliab 9:263–282CrossRef

Pei M, Goodman ED, Gao Z, Zhong K (1994) Automated software test data generation using a genetic algorithm. Technical report, Michigan State University

Pocatilu P, Ivan I (2013) A genetic algorithm based system for automatic control of test data generation. Stud Inf Control 22(2):219–226

Rauf A, Anwar S, Jaffer MA, Shahid A (2010) Automated GUI test coverage analysis using GA. In: Proc. 7th international conference on information technology, pp. 1057–1062

Roper M, Maclean I, Brooks A, Miller J, Wood M (1995) Genetic Algorithms and the automatic generation of test data. Technical report RR/95/195[EFoCS-19-95], Department of Computer Science, University of Strathclyde

Shamshiri S, Rojas JM, Fraser G, McMinn P (2015) Random or genetic algorithm search for object-oriented test suite generation?. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 1367–1374

Sharma N, Pasala A, Kommineni R (2012) Generation of character test input data using GA for functional testing. In: Proc. 19th APSEC-SATA workshop, pp. 87–94

Sofokleous AA, Andreou AS (2008) Automatic, evolutionary test data generation for dynamic software testing. J Sys Softw 81:1883–1898CrossRef

Srinivas M (1994) Genetic algorithms: a survey. J Comput 27:17–26CrossRef

Sthamer HH (1996) The automatic generation of software test data using genetic algorithms. Ph. D thesis, University of Glamorgan, Pontypridd, Wales

Suresh Y, Rath SK (2013a) A genetic algorithm based approach for test data generation in basis path testing. In: International Journal of Soft Computing and Software Engineering [JSCSE], Special Issue: The proceeding of international conference on soft computing and software engineering, 3(3)

Suresh Y, Rath S (2013b) Application of meta-heuristic algorithms for automated software test data generation. Int J Comput Intell Stud 4(2):113–133

Tracey N (2000) A search-based automated test data generation framework for safety critical software. Ph. D. thesis, University of York

Watkins A (1995) The automatic generation of test data using genetic algorithms. In: Proc. 4th software quality conference, pp. 300–309

Wegener J, Baresel A, Sthamer H (2001) Evolutionary test environment for automatic structural testing. J Inf Softw Technol 43:841–854CrossRef

Xanthakis S, Ellis C, Skourlas C, Le Gall A, Katsikas S, Karapoulios K (1992) Application of genetic algorithms to software testing. In: Proc. 5th ICSE, pp. 625–636

Xiao J, Afzal W (2010) Search-based resource scheduling for bug fixing tasks. In: Proc. 2nd SSBSE, pp. 133–14

Xue-ying MA, Sheng BK, Zhen-feng HE, Cheng-qing YE (2005) A genetic algorithm for test-suite reduction. In: Proc. ICSMC, pp. 133–139

Zhang N, Wu B, Bao X (2015) Automatic generation of test cases based on multi-population genetic algorithm. Int J Multimed Ubiquitous Eng 10(6):113–122CrossRef

Zhang W, Gong D, Yao X, Zhang Y (2007) Evolutionary generation of test data for many paths coverage. In: Proc. Chinese Control and Decision Conference, pp. 230–235

Titel: Evolution or revolution: the critical need in genetic algorithm based testing
verfasst von: Anupama Surendran
Philip Samuel
Publikationsdatum: 27.08.2016
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 3/2017
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9504-8

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