American Journal of Information Science and Computer Engineering
Articles Information
American Journal of Information Science and Computer Engineering, Vol.1, No.1, May 2015, Pub. Date: Jun. 6, 2015
Optimization the Test Suite of Regression Testing Using Metaherustic Searching Technique
Pages: 10-20 Views: 4176 Downloads: 1342
Authors
[01] Aakanksha Pandey, Computer Science & Engineering, SRM University, NCR Campus, Modinagar, Ghaziabad, India.
[02] Jayant Shekher, Computer Science & Engineering, Subharti University, Meerut, India.
Abstract
This proposed technique investigates is for the reduction of the test suit with the use of metaheuristic approach this technique is known as genetic algorithm. The result is showing like with the help of regression testing we can reduce the size n cost of the test suit significantly the very important features of the test suit that we need to take in consideration is “test suit reduction”. Here we have uses the algorithm that is the combination of the test-execution cost criteria and block based coverage criteria, these new criteria with that we can make the prominent decision for reducing the test suit. Here for the test-suit coverage criteria other criteria such as risk or fault-detection effectiveness, or combination of this criterion we have used the approach is greedy algorithm that is the sub set selection problem which is NP complete.
Keywords
Metaheuristic Approach, Genetic Algorithm, Regression Testing, Test Suite
References
[01] Anoj Kumar, 2Shailesh Tiwari, 3 K. K. Mishra and 4A.K. Misra, Generation of Efficient Test Data using Path Selection Strategy with Elitist GA in Regression Testing,IEEE 2007,PP 43 -51.
[02] Agastya Nanda_, Senthil Mani†, Saurabh Sinha†, Mary Jean Harrold‡, and Alessandro Orso‡, Regression Testing in the Presence of Non-code Changes,IEEE 2011,PP 211-218.
[03] Bing JIANG, Yongmin MU, Research of Optimization Algorithm for Path-Based Regression Testing Suit,IEEE 2011, PP 122-128.
[04] Dennis Jeffrey and Neelam Gupta, Improving Fault Detection Capability By Selectively Retaining Test Cases during Test Suite Reduction, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 33, NO. 2, FEB- 2007, PP108-127.
[05] Engin Uzuncaova, Sarfraz Khurshid, and Don Batory, Incremental Test Generation for Software Product Lines, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 36, NO. 3, MAY/JUNE 2010 PP 309-321.
[06] Gregory M. Kapfhammer, Empirically Evaluating Regression Testing Techniques: Challenges, Solutions, and a PotentialWay Forward,IEEE 2011,PP 78-84.
[07] Hyunsook Do, Ladan Tahvildari, The Effects of Time Constraints on Test Case Prioritization, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 36, NO. 5, SEPTEMBER/OCTOBER 2010PP 593-614.
[08] Irman Hermadi, Chris Lokan, Genetic Algorithm Based Path Testing:Challenges and Key Parameters, 2010 Second WRI World Congress on Software Engineering PP 341-356.
[09] James H. Andrews, Genetic Algorithms for Randomized Unit Testing, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 37, NO. 1, JANUARY/FEBRUARY 2011, PP 80-102.
[10] Kaner.C, J. Falk, and H.Q. Nguyen H.Q. Testing Computer Software,2nd Edition, John Wiley & Sons, April, 1999.
[11] Kitchenham, B.A., Pfleeger, S.L., Pickard, L.M., Preliminary Guidelines for Empirical Research in Software Engineering,IEEE 2005,PP 18-24.
[12] Mary Jean Harrold, ,Empirical Studies of a Prediction Model for Regression Test Selection, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 27, NO. 3, MARCH 2001, PP 248-260.
[13] Mark Harman, Kiran Lakhotia, Phil McMinn, A Multi–Objective Approach To Search–Based Test Data Generation,IEEE 2008,PP 98-105.
[14] Nigel Tracey John Clark Keith Mander, Automated Program Flaw Finding using Simulated Annealing,IEEE 2007,PP 201-208.
[15] Lyu M.R, eds., Handbook of Software Reliability Engineering, McGraw-Hill, 1996.
[16] Pavan Kumar Chittimalli and Mary Jean Harrold, Senior Member, Recomputing Coverage Information to Assist Regression Testing, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 35, NO. 4, JULY/AUGUST 2009, PP 452-472.
[17] Phil McMinn, Search-based Software Test Data Generation:A Survey,WHITE PAPER.
[18] Preeyavis Pringsulaka and Jirapun Daengdej, Coverall Algorithm for Test Case Reduction,IEEE 2005 ,PP 234-239.
[19] P fleeger.S.L, Software Engineering Theory and Practice, Prentice Hall, 2001.
[20] Stefan Wappler, Ina Schieferdecker, Improving Evolutionary Class Testing in the Presence of Non-Public Methods,IEEE 2004,PP 308-312.
[21] Simon Poulding and John A. Clark Efficient Software Verification: Statistical Testing Using Automated Search, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 36, NO. 6, NOVEMBER/DECEMBER 2010, PP 763-787.
[22] Shaukat Ali, C. Briand, Hadi Hemmati, A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 36, NO. 6, NOVEMBER/DECEMBER 2010.
[23] Ummu Salima.T.M.S,Ms. A.Askarunisha, Dr. N.Ramaraj, Enhancing The Efficiency Of Regression TestingThrough Intelligent Agents, International Conference on Computational Intelligence and Multimedia Applications 2007,PP 230-238.
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