This paper is published in Volume-4, Issue-3, 2018
Area
Software Testing
Author
Sumedha Raheja, Rajvir Singh
Org/Univ
Deenbandhu Chhotu Ram University of Science and Technology, Sonipat, Haryana, India
Pub. Date
04 June, 2018
Paper ID
V4I3-1599
Publisher
Keywords
Software testing, Regression testing, Bat algorithm, Cuckoo search algorithm, Software maintenance

Citationsacebook

IEEE
Sumedha Raheja, Rajvir Singh. A mapping study on test case selection based on nature-inspired algorithms, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sumedha Raheja, Rajvir Singh (2018). A mapping study on test case selection based on nature-inspired algorithms. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Sumedha Raheja, Rajvir Singh. "A mapping study on test case selection based on nature-inspired algorithms." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

After delivery of software product for modification, in order to correct faults or for improving the performance or other attributes, we calculate software maintenance. For this there is the need for regression testing, regression testing is used to check that no upcoming errors have been found throughout the maintenance phase. The abundant number of test suites consist of some repetitions/redundancies as the same fault/error may be covered by many test cases. Hence, it is recommended/advisable to decrease/reduce the test suite. Test case selection is one of the techniques used for reducing the number of test cases by selecting only those test cases from test suite which can detect all those faults which were detected by the whole test. This paper calculates the execution/performance of two metaheuristic algorithm – Cuckoo search and Bat algorithm for selecting test cases. Performance evaluation deciding factors are no. of faults detected and execution time. Results are achieved by conducting experiments on a large scale.