Swarm Intelligence Techniques and Genetic Algorithms for Test Case Prioritization
Tina Sachdeva

Tina Sachdeva*, Department of Computer Science, Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, Delhi, India.

Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 465-469 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6810049420/2020©BEIESP | DOI: 10.35940/ijeat.D6810.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Regression testing is a technique which is carried out to ascertain that the changes that were done in the source code have not negatively damped its performance. Hence, it is a crucial and an expensive step of the software development life cycle. It re-establishes confidence in correctness of the software after changes were made to it. A test suite is used to test the software, but often it becomes time consuming to re-execute each test case every time regression testing is done. Therefore, it becomes essential to decrease the number of the test cases by prioritizing them based on some criterion. This ensures maximum detection of faults in least amount of time. In this paper, author has compared swarm intelligence techniques with genetic algorithms for such a test suite prioritization. In particular, by taking a sample GCD program Ant Colony Optimization (ACO) has been compared with Genetic Algorithms (GA) for the purpose of test suite minimization. Unit of comparison has been execution time required for prioritization of test cases. Further, experimental results have been compared with time taken by both with random testing. 
Keywords: Test Case Prioritization, Regression Testing, Swarm Optimization, Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm