Nitish Mittal

Work place: Netaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi-110078, India



Research Interests: Software Notations and Tools, Autonomic Computing, Data Mining, Data Structures and Algorithms, Mathematics of Computing


Nitish Mittal received his B.E in Computer Science from NSIT, University of Delhi, India in 2016. He is currently working as Software engineer in, Dubai, UAE. His areas of interest include software testing, soft computing and data mining.

Author Articles
A Hybrid Artificial Bee Colony and Harmony Search Algorithm to Generate Covering Arrays for Pair-wise Testing

By Priti Bansal Sangeeta Sabharwal Nitish Mittal

DOI:, Pub. Date: 8 Aug. 2017

Combinatorial Interaction Testing (CIT) is a cost effective testing technique that aims to detect interaction faults generated as a result of interaction between components or parameters in a software system. CIT requires the generation of effective test sets that cover all possible t-way (t denotes the strength of testing) interactions between parameters. Covering array (CA) and mixed covering array (MCA) are often used to represent test sets. This paper presents a hybrid algorithm that integrates artificial bee colony algorithm (ABC) and harmony search algorithm (HS) to construct CAs for testing all 2-way interactions (pair-wise testing) in software systems. The performance of the proposed hybrid algorithm ABCHS-CAG is compared and analyzed by performing experiments on a set of benchmark problems on pair-wise testing. The results show that ABCHS-CAG generates smaller CAs than its greedy counterparts whereas its performance is comparable to the existing state-of-the-art meta-heuristic algorithms.

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Construction of Strength Two Mixed Covering Arrays Using Greedy Mutation in Genetic Algorithm

By Sangeeta Sabharwal Priti Bansal Nitish Mittal

DOI:, Pub. Date: 8 Sep. 2015

Metaheuristic methods are capable of solving a wide range of combinatorial problems competently. Genetic algorithm (GA) is a metaheuristic search based optimization algorithm that can be used to generate optimal Covering Arrays (CAs) and Mixed Covering Arrays (MCAs) for pair-wise testing. Our focus in the work presented in this paper is on the strategies of performing mutation in GA to enhance the overall performance of GA in terms of solution quality and computational time (number of generations). This is achieved by applying a greedy approach to perform mutation at a position that minimizes the loss of existing distinct pairs in the parent CA/MCA and ensures that the generated offspring is of good quality. Experiments are conducted on several benchmark problems to evaluate the performance of the proposed greedy based GA with respect to the existing state-of-the-art algorithms. Our evaluation shows that the proposed algorithm outperforms its GA counterpart by generating better quality MCA in lesser number of generations. Also the proposed approach yields better/comparable results compared to the existing state-of-the-art algorithms for generating CAs and MCAs.

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