A Stochastic Method for Test Case Selection in Software Testing
Nithya T.M1, Chitra. S2
1Mrs. Nithya T.M, Assistant Professor, Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Trichy (Tamil Nadu), India.
2Dr. Chitra. S, Principal, Er. Perumal Manimekalai College of Engineering, Hosur (Tamil Nadu), India.
Manuscript received on 20 January 2020 | Revised Manuscript received on 02 February 2020 | Manuscript Published on 05 February 2020 | PP: 265-271 | Volume-8 Issue-4S5 December 2019 | Retrieval Number: D10551284S519/2019©BEIESP | DOI: 10.35940/ijrte.D1055.1284S519
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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: The quality of the software is a very important aspect in the development of software application. In order to make sure there is the software of good quality, testing is a critical activity of software development. Thus, software testing is the activity which focuses on the computation of an attribute or the ability of either a system or program that decides if user requirements are met. There is a proper strategy for the design of software for which testing has to be adopted. The techniques of test case selection attempt at reduction of the test cases that need to be executed at the same time satisfying the needs of testing that has been denoted by the test criteria. In the time of software testing, and the resource will be the primary constraints at the time of testing since this has been a highly neglected phase in the Software Development Life Cycle (SDLC). The optimizing of a test suite is very critical for the reduction of the testing phase and also the selection of the test cases that eliminate unwanted or redundant data. All work in literature will make use of techniques of single objective optimization that does not have to be efficient as the code coverage will play an important role at the time of selection of test case. As the test case choice is Non-Deterministic, the work also proposes a novel and multi-objective algorithm like the Non-Dominated Sorting Genetic Algorithm II (NSGA II) and the Stochastic Diffusion Search (SDS) algorithm that makes use of the cost of execution and code coverage as its objective function. The results prove a faster level of convergence of the algorithm with better coverage of code in comparison to the NSGA II.
Keywords: Multi- Objective Optimization, Non-dominated Sorting Genetic Algorithm II (NSGA II) Stochastic Diffusion Search (SDS), Software Testing, Test Case Selection.
Scope of the Article: Systems and Software Engineering