D. Jeya Mala
Abstract
Software development organizations spend considerable portion of their budget and time in testing related activities. The effectiveness of the verification and validation process depends upon the number of errors found and rectified before releasing the software to the customer side. This in turn depends upon the quality of test cases generated. The solution is to choose the most important and effective test cases and removing the redundant and unnecessary ones; which in turn leads to test case optimization. To achieve test case optimization, this paper proposed a heuristics guided population based search approach namely Hybrid Genetic Algorithm (HGA) which combines the features of Genetic Algorithm (GA) and Local Search (LS) techniques to reduce the number of test cases by improving the quality of test cases during the solution generation process. Also, to evaluate the performance of the proposed approach, a comparative study is conducted with Genetic Algorithm and Bacteriologic Algorithm (BA) and concluded that, the proposed HGA based approach produces better results.
- Eric W.W., Joseph R.H., Saul L. and Aditya P. Mathur (1998), 'Effect of Test Set Minimization on Fault Detection Effectiveness', Software Practice & Experience, Vol. 28, No. 4, pp. 347--369. Google Scholar
Digital Library
- Aditya P.Mathur (2008), 'Foundations of Software Testing', Pearson Education. Google ScholarDigital Library
- Jeremy S. Bradbury (2006), 'Using Mutation for the Assessment and Optimization of Tests and Properties', Technical Report 2006, p.518.Google Scholar
- Bruno T.D.A., Eliane M. and Fabiano L.D.S. (2007), 'Generalized Extremal Optimization: An Attractive Alternative for Test Data Generation', GECCO-2007, p.1137.Google Scholar
- Offutt J., Ma Y.S. and Kwon Y.R. (2004), 'An Experi-mental Mutation System for Java', ACM SIGSOFT Software Engineering Notes, Vol. 29, No. 5, pp. 1--4. Google ScholarDigital Library
- Mcminn P., Harman M., Binkley D. and Paolo Tonella (2006), 'The Species per Path Approach to Search Based Test Data Generation', ISSTA-2006, pp.13--24. Google ScholarDigital Library
- Pargas R.P. Harrold M. and Peck R. (1999), 'Test-Data Generation Using Genetic Algorithms', Software Testing, Verification and Reliability, Vol. 9, No. 4, pp. 263--282.Google ScholarCross Ref
- Baudry B., Fleurey F., Le Traon Y. and Jézéquel J.M. (2005), 'An Original Approach for Automatic Test Cases Optimization: A Bacteriologic Algorithm'. IEEE Software, Vol. 22, Google ScholarDigital Library
- Banks D., Dashiell W., Gallagher L., Hagwood C., Kacker R. and Rosenthal L. (1998), 'Software Testing By Statistical Methods Preliminary Success Estimates For Approaches Based On Binomial Models, Coverage Designs, Mutation Testing, And Usage Models', Technical Report -- NIST.Google ScholarCross Ref
- Prowell S.J. (2004), 'A Stopping Criterion for Statistical Testing', In Proceedings of the 37th Hawaii International Conference on Systems Sciences (HICSS'37), Kona, HI. Google ScholarDigital Library
- Ostrand T.J., Weyuker E.J. and Bell R.M. (2005), 'Predicting The Location And Number Of Faults In Large Software Systems', IEEE Transactions on Software Engineering, Vol. 31, No. 4, pp. 340--355. Google ScholarDigital Library
- Ramon S. and Jose A.L. (2006), 'Scatter Search in Software Testing, Comparison and Collaboration with Estimation of Distribution Algorithms', European Journal of Operational Research, Vol. 169, No. 2, pp. 392--412.Google ScholarCross Ref
- Ramamoorthy C.V., Siu-Bun F. Ho and W.T. Chen.(1976), 'On The Automated Generation of Program Test Data', IEEE Transactions on Software Engineering, Vol. 2, No. 4, pp. 293--300. Google ScholarDigital Library
- Korel B. (1990), 'Automated Software Test Data Generation', IEEE Transaction on Software Engineering, Vol. 16, No. 8, pp. 870--879. Google ScholarDigital Library
- Ferguson R. and Korel B. (1996), 'The Chaining Approach For Software Test Data Generation', ACM Trans-actions on Software Engineering And Methodology (TOSEM), Vol. 5, No. 1, pp. 63--86. Google ScholarDigital Library
- Roper M., Maclean I., Brooks A., Miller J. and Wood M. (1995), 'Genetic Algorithms and the Automatic Generation of Test Data', Technical Report Rr/95/195 Dept. Computer Science, University of Strathclyde.Google Scholar
- Sthamer H.H. (1996), 'The Automatic Generation of Software Test Data using Genetic Algorithms', Ph.D Thesis, University of Glamorgan, Pontyprid, Wales, Great Britain.Google Scholar
- Jones B., Eyres D. and Sthamer H. (1998), 'A Strategy for Using Genetic Algorithms to Automate Branch and Fault-Based Testing', The Computer Journal, Vol. 41, No. 2, pp. 98--107.Google ScholarCross Ref
- Jones B.F., Sthamer H.H. and Eyres D. (1996), 'Auto-matic Structural Testing Using Genetic Algorithms', Software Engineering Journal. Vol. 11, No. 5, pp. 299--306.Google ScholarCross Ref
- Pargas R.P. Harrold M. and Peck R. (1999), 'Test-Data Generation Using Genetic Algorithms', Software Testing, Verification and Reliability, Vol. 9, No. 4, pp. 263--282.Google ScholarCross Ref
- Bottaci L. (2001), 'A Genetic Algorithm Fitness Function For Mutation Testing', Proceedings of the Seminall-Workshop at the 23rd International Conference on Software Engineering.Google Scholar
- Paolo Tonella (2004), 'Evolutionary Testing of Classes', ISSTA-2004, pp. 11--14. Google ScholarDigital Library
- Mcminn P. and Holcombe M. (2003), 'The State Problem For Evolutionary Testing', Proceedings of GECCO 2003, Lecture Notes In Computer Science, Vol. 2724, pp. 2488--2500. Google ScholarDigital Library
- Mcminn P. (2004), 'Search-Based Software Test Data Generation: A Survey', Software Testing, Verification and Reliability, Vol. 14, No. 2, pp. 105--156. Google ScholarDigital Library
- Xie T., Marinov D. and Notkin D. (2004), 'Rostra: A Framework for Detecting Redundant Object-Oriented Unit Tests', Proceedings of the 19th IEEE International Conference on Automated Software Engineering, Pp.196--205, 2004. Google ScholarDigital Library
- Last M. Eyal S. and Kandel A.(2006), 'Effective Black-Box Testing with Genetic Algorithms', Book on Hardware and Software, Verification And Testing, Lecture Notes In Computer Science, pp. 134--148. Google ScholarDigital Library
- Bruno T.D.A., Eliane M. and Fabiano L.D.S. (2007), 'Generalized Extremal Optimization: An Attractive Alternative for Test Data Generation', GECCO-2007, p.1137.Google Scholar
- Binder R.V.(2000), 'Testing Object-Oriented Systems: Models, Patterns, and Tools', Addison-Wesley. Google ScholarDigital Library
- Natalio K. and Jim S. (2005), 'A Tutorial on Competent Memetic Algorithms: Model, Taxonomy and Design Issues', IEEE Transactions on Evolutionary Computation, Vol. Ano.B.CCC200D.Google Scholar
- Vincent K., Florin C., Oliver L. and Louis W. (2008), 'An Hybrid Optimization Technique Coupling Evolutionary And Local Search Algorithms', Journal of Computational And Applied Mathematics, Vol. 215, No. 2, pp. 448--456. Google ScholarDigital Library
- Land M. (1998), 'Evolutionary Algorithms with Local Search for Combinatorial Optimization', Ph.D. Thesis, University Of California, San Diego. Google ScholarDigital Library
Index Terms
- Quality improvement and optimization of test cases: a hybrid genetic algorithm based approach
Recommendations
Optimize SPL test cases with adaptive simulated annealing genetic algorithm
ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - ChinaIn Software Product Line (SPL) testing, reduced test suite with high coverage is useful for early features interaction detection. sGA (simplified genetic algorithm) and SAGA(simulated annealing genetic algorithm) can generate high coverage test suite. ...
A Hybrid Genetic Algorithm for Weapon Target Assignment Optimization
ISMSI '18: Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm IntelligenceAs a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of ...
On the performance of a hybrid genetic algorithm in dynamic environments
The ability to track the optimum of dynamic environments is important in many practical applications. In this paper, the capability of a hybrid genetic algorithm (HGA) to track the optimum in some dynamic environments is investigated for different ...
Comments