On the Design and Optimization of Test Cases Using an Improved Artificial Bee Colony Algorithm-Based Swarm Intelligence Approach

On the Design and Optimization of Test Cases Using an Improved Artificial Bee Colony Algorithm-Based Swarm Intelligence Approach

Jeya Mala D., Ramalakshmi Prabha M.
Copyright: © 2022 |Pages: 20
DOI: 10.4018/IJSIR.309941
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Abstract

In this research work, a swarm intelligence-based approach, namely an improved artificial bee colony (IABC), has been proposed to design and optimize the test cases during the software testing process. The novelty of the proposed IABC algorithm is that it has three major improvement heuristics over the general ABC algorithm: (1) it replaces random population generation during the initial phase into a systematic initial solution generation by means of a novel heuristic, namely ‘Chaotic Map'; (2) to eliminate the redundant test cases, another novel heuristic, namely ‘Euclidean Distance', is applied to maintain the diversity of population; (3) to increase the convergence speed, the fitness value of the previous solution is used in the new solution generation. Further, the proposed algorithm has been evaluated with several case studies and compared with the existing works using path coverage-based test adequacy criterion. Hence, the proposed work is improved, and it outperforms the existing works and provides optimal or near optimal test case generation for efficient software testing.
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Importance Of Abc In Optimization Problems

Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. It is as simple as Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, and uses only common control parameters such as colony size and maximum cycle number (Karaboga and Basturk 2008).

ABC as an optimization tool provides a population-based search procedure in which individuals called foods positions are modified by the artificial bees with time and the bee’s aim is to discover the places of food sources with high nectar amount and finally the one with the highest nectar.

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