Open Access Open Access  Restricted Access Subscription Access

An Insight to Software Testing using Genetic Algorithm

Reena ., Bhawna ., Pradeep Kumar Bhatia


Many techniques have been discovered by the researcher’s based on the minimization of test suites. Software testing ingests about 50% of the total time, cost and resources and this time is even higher if the system is safety critical. This is because the code scope of Software under Test (SUT) is very big. Numerous test cases can be produced for a small piece of code and the testing of all these test suites is not feasible due to the availability of limited time and resources. So, to save the time and resources these test cases should be minimized by choosing the group of test suites that has the greatest likelihood of uncovering the bugs. This paper is focused on minimization of test cases using Genetic Algorithms. This paper also presents the comparison of various testing techniques. The results of two techniques GA with sigma scaling and GA with class partitioning are compared. On the basis of fitness value both the techniques are evaluated. Minimum and maximum fitness values are 44 and 204 using technique 1 (GA with sigma scaling) while 76 and 236 are minimum and maximum fitness values using technique 2 (GA with class partitioning). The goal of this study is to provide the direction to software developer about various testing techniques.

Full Text:



Arvinder Kaur; Shubhra Goyal; A Genetic Algorithm for Fault-based egression Test Case Prioritization, International Journal of Computer Applications, 32(8) 1839-1847(2011).

D. Berndt; J. Fisher; L. Johnson; J. Pinglikar; A. Watkins; Breeding Software Test Cases with Genetic Algorithms, Hawaii International Conference on System Science, (2003).

D. J. Mala; V. Mohan; E. Ruby; A Hybrid Test Optimization Framework-Coupling Genetic Algorithm with Local Search Technique, Computing and Informatics, 29(1) 133-164(2010).

F. A. Sadjadi; Comparison of fitness scaling

F. He; J. Len; H. Zhang; Y. Tan; Evolutionary Testing of Trusted Computing Supporting Software Based on Genetic Algorithms, IEEE, 713-717 (2008).

function in genetic algorithms with applications to optical processing, Optical Information Systems 2, 356-364(2004).

G.S.V.P. Raju; V. Sumanlatha; Object Oriented Test Case Generation Technique using Genetic Algorithms, International Journal of Computer Applications, 61(20) 20-26(2013).

Gaurav Kumar; Pradeep Kumar Bhatia; Software Testing Optimization through Test Suite Reduction using Fuzzy Clustering, CSI Transactions on ICT, 1(3) 253-260(2013).

J. Clark; J. J. Dolado; M. Harman; R. Hierons; Reformulating Software Engineering as a search problem, IEEE Proceedings- Software, 150(3) 161-175(2003).

J. T. Alander; T. Mantere; P. Turunen; Genetic Algorithm based Software Testing, Artificial neural nets and genetic algorithms, Springer, 325-328 (1998).

Jain, Pandey; Soft Computing based Approaches for Software Testing, International Journal of Soft Computing and Engineering, 4(2) 4-8,(2014).

K. Böhmer; S. Rinderle-Ma; A genetic algorithm for automatic business process test case selection, On the Move to Meaningful Internet Systems International Conferences, 166-184(2015).

K.K. Aggarwal; Y. Singh; Software Engineering Programs Documentation, Operating Procedures, New Age International Publishers, (2005).

L. Bing; Z. Chen; Pivotal techniques of embedded software testing case generation by genetic algorithms, IEEE, 1-5(2006).

Li; K. Zhang; Z. Kou; Breeding software test data with genetic-particle swarm mixed algorithm, Journal of computers, 5(2), 258-265(2010).

M. Alzabidi; A. Kumar; A. D. Shaligram; Automatic Software Structural Testing by Using Evolutionary Algorithms for Test Data Generations, International Journal of Computer Science and Network Security, 9(4) 390-395(2009).

M. Pedemonte; F. Luna; E. Alba; Systolic genetic search-a systolic computing-based metaheuristic, Soft Computing, 19(7) 1779-1801(2015).

N. K. Gupta; M. K. Rohil; Using Genetic Algorithm for Unit Testing of Object Oriented Software, International Journal of Simulation: Systems, Science and Technology, 10(3) 308-313(2008).

R. Murugan; M. R. Mohan; Artificial bee colony optimization for the combined heat and power economic dispatch problem, ARPN Journal of Engineering and applied sciences, 7(5) 597-604(2012).

S. Raju; G. V. Uma; Factors Oriented Test Case Prioritization Technique in Regression Testing using Genetic Algorithm; European Journal of Scientific Research, 74(3) 389-402(2012).

S. Eyal; A. Kandel; M. Last; Effective lack-Box Testing with Genetic Algorithms, Springer-Verlag Berlin Heidelberg, (2005).

S. Mittal; O. P. Sangwan; Metaheuristic-Based Approach to Regression Testing, International Journal of Computer Science and Information Technologies, 6(3) 2597-2605(2015).

S. Mittal; O. P. Sangwan; Metaheuristic-based Approach to Regression Testing, International Journal of Computer Science and Information Technologies, 6(3) 2597-2605(2015).

S. Selvakumar; M. R. C. Dinesh; C. Dhineshkumar; N. Ramaraj, Reducing the Size of the Test Suite by Genetic Algorithm and Concept Analysis, In Recent Trends in Networks and Communications, 153-161(2010).

T. Dwivedi; J. Srivastava; Software Testing Strategy Approach on Source Code Applying Conditional Coverage Method, International Journal of Software Engineering and its Applications, 6(3) 25-31(2015).

T. H. Kim; P. R. Srivastava; Application of Genetic Algorithm in Software Testing, International Journal of Software Engineering and Its Applications, 3(4) 87-96(2009).

Y.Y. Chen; K.X. Wei; G. Gong; X.T. Hu; Parameters selection of fitness scaling in genetic algorithm and its applications, IEEE, 2475-2480(2010).

Z. Michaelewicz; Genetic Algorithms + Data Structures = Evolution Programs, Springer, (1995).


  • There are currently no refbacks.



© 2015 IARS. All right reserved.