An Automated Testing Approach in Data Mining System using Genetic Algorithm Framework

Software testing performances are usually designed by human experts, while test automation tools are appraisal of test outcomes is also associated with a considerable effort by software testers who may have imperfect knowledge of the requirements specification. This paper presents a method for optimizing software testing efficiency by identifying the most critical path clusters in a program. The factors discovered are used in evaluating the fitness function of Genetic algorithm for selecting the best possible Test method. This integration will help in improving the common performance of genetic algorithm in search space exploration and exploitation fields with improved convergence rate. To improve testing productivity and reduce costs, it is highly desirable to automate test generation and execution. The extensive software testing is infrequently feasible because it becomes difficult for even medium sized software. Typically only parts of a program can be tested, but these parts are not essentially the most error prone. Consequently, we are developing a additional selective approach to testing by focusing on those parts that are most significant so that these paths can be tested first. By identifying the most significant paths, the testing efficiency can

[1]  M. Kumar,et al.  Generation of test data using meta heuristic approach , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[2]  Ajeet Kumar,et al.  SYSTEMATIC STUDY OF A WEB TESTING TOOL: SELENIUM , 2013 .

[3]  Edgar E. Vallejo,et al.  A clustering genetic algorithm for inferring protein-protein functional interactions from phylogenetic profiles , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[4]  Tom Mens,et al.  A survey of software refactoring , 2004, IEEE Transactions on Software Engineering.

[5]  Jeff Yu Lei,et al.  Combinatorial Software Testing , 2009, Computer.

[6]  Chen Wang,et al.  Automatic generation of test data for path testing by adaptive genetic simulated annealing algorithm , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[7]  Mark Last,et al.  A compact and accurate model for classification , 2004, IEEE Transactions on Knowledge and Data Engineering.

[8]  Dingwei Wang,et al.  A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[9]  Nashat Mansour,et al.  Data Generation for Path Testing , 2004, Software Quality Journal.

[10]  Vinay Chopra,et al.  Design and Implementation in Selenium IDE with Web Driver , 2012 .

[11]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[12]  D. J. Berndt,et al.  High Volume Software Testing using Genetic Algorithms , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[13]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .