Loading

An Algorithm for Finding the Optimal Path In Basis Path Testing using GABVIE Model
Seema Sharma1, Shaveta Bhatia2

1Seema Sharma*, FCA, Manav Rachna international Institute of Research and Studies, Faridabad, India.
2Dr. Shaveta Bhatia, FCA, Manav Rachna international Institute of Research and Studies, Faridabad, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 587-593 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8436019320/2020©BEIESP | DOI: 10.35940/ijitee.C8436.019320
Open Access | Ethics and Policies | Cite | Mendeley
© 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: ISoftware testing is one of the most vital factors in software development life cycle. It is mainly used for testing the program code, known as white box testing and to test the functionality of the program, known as black box testing. Manual generation of test data is very costly, error vulnerable and time consuming task. Subsequently, there is a need to make the process automated as could be expected under the circumstances. This paper presented the automated generation of optimal path with intention of attaining the maximum coverage. The work being done considers the optimal path coverage in minimum cost. The task of generating test cases can be done through the concept Genetic Algorithm with importance of variable (GABVIE). The proposed algorithm additionally considers programs having numerous modules. This is vital as a large portion of the current test data generators not succeeded to establish the communication between the modules. The approach has been implemented on various program code and the outcomes got have been confirmed. The proposed work considers the white box testing. 
Keywords: Variables, Test Cases, White Box Testing, Genetic Algorithm
Scope of the Article: Algorithm Engineering