Combined Genetic and Simulated Annealing Approach for Test Case Prioritization

Background: The test case prioritization of regression Testing is described. Methods: A new test case prioritization algorithm is proposed to get better the rate of fault detection and cost reduction. Heuristic Techniques like Genetic Algorithm (GA) and Simulated Annealing (SA) are employed. It prioritizes the test cases depends on fault detection ability and execution time taken. Findings: The implementation of proposed algorithm in JAVA is found to produce optimal or near optimal results. The efficiency of proposed regression testing technique is proved by comparing it with GA and SA methods individually. Applications: A complete automation tool for the complete usage of the algorithm is being developed. It will also be analyzed on larger projects with large number of test cases and faults.

[1]  Arvinder Kaur,et al.  A Hybrid Approach for Regression Testing in Interprocedural Program , 2010, J. Inf. Process. Syst..

[2]  R. Uma Maheswari,et al.  A novel approach for test case prioritization , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[3]  Gregg Rothermel,et al.  Test Case Prioritization: A Family of Empirical Studies , 2002, IEEE Trans. Software Eng..

[4]  Willy Susilo,et al.  On the Fault-Detection Capabilities of Adaptive Random Test Case Prioritization: Case Studies with Large Test Suites , 2012, 2012 45th Hawaii International Conference on System Sciences.

[5]  Man Fai Lau,et al.  Fault-based test suite prioritization for specification-based testing , 2012, Inf. Softw. Technol..

[6]  S. Indhumathi,et al.  Improving Coverage Deployment for Dynamic Nodes using Genetic Algorithm in Wireless Sensor Networks , 2015 .

[7]  Rajib Mall,et al.  An approach to prioritize the regression test cases of object-oriented programs , 2013, CSI Transactions on ICT.

[8]  Nadine Mandran,et al.  Prioritizing test cases with string distances , 2011, Automated Software Engineering.

[9]  D. Jeya Mala,et al.  Critical components identification and verification for effective software test prioritization , 2011, 2011 Third International Conference on Advanced Computing.

[10]  Mark Harman,et al.  A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search , 2010, IEEE Transactions on Software Engineering.

[11]  D. Jeya Mala,et al.  On the Use of Intelligent Agents to Guide Test Sequence Selection and Optimization , 2009, Int. J. Comput. Intell. Appl..

[12]  Xiaorong Xie.  Genetic Algorithm and Simulated Annealing: A Combined Intelligent Optimization Method and Its Application to Subsynchronous Damping Control in Electrical Power Transmission Systems , 2012 .