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Research Paper | Computer Science & Engineering | India | Volume 4 Issue 10, October 2015 | Rating: 6.6 / 10
Test Suite optimization Using Artificial Bee Colony and Adaptive Neural Fuzzy Inference System
Gurcharan Kaur | Bhupender Yadav
Abstract: Software test suite optimization is one of the essential issue in software engineering analysis. This paper deals with the improvement in quality of software by software Test Suite Optimization using Artificial Bee Colony (ABC) based novel search technique and technique determine the software development time accurately by proposed Adaptive Neuro Fuzzy Inference System (ANFIS). In this approach, ABC combines the equidistant behaviour of these three bees makes generation of feasible self-supporting paths and also makes software test suite optimization faster. Test Cases are generated using Test Path Sequence Comparison Method as the fitness value objective function. This research also presents an approach for the automated generation of feasible self-supporting test path based on the priority of all edge cover.
Keywords: ABC, ANFIS, SDLC, SUT
Edition: Volume 4 Issue 10, October 2015,
Pages: 721 - 723