Quantification of Software Code Coverage Using Artificial Bee Colony Optimization Based on Markov Approach

Boopathi, M.; Sujatha, R.; Kumar, C. Senthil; Narasimman, S.

doi:10.1007/s13369-017-2554-7

Cited by 16 publications

(6 citation statements)

References 22 publications

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“…At each source of food, there is a single employed bee at that place. The food source quality, which is associated with the location, represents the fitness value [16]. The technique employed by bees to find sources of food is utilized to locate the best solution [17].…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

Shabat

Raheem

2023

IJEECS

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Combinatorial optimization problems are problems that have a large number of discrete solutions and a cost function for evaluating those solutions in comparison to one another. With the vital need of solving the combinatorial problem, several research efforts have been concentrated on the biological entities behaviors to utilize such behaviors in population-based metaheuristic. This paper presents bee colony algorithms which is one of the sophisticated biological nature life. A brief detail of the nature of bee life has been presented with further classification of its behaviors. Furthermore, an illustration of the algorithms that have been derived from bee colony which are bee colony optimization, and artificial bee colony. Finally, a comparative analysis has been conducted between these algorithms according to the results of the traveling salesman problem solution. Where the bee colony optimization (BCO) rendered the best performance in terms of computing time and results.

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

Shabat

Raheem

2023

IJEECS

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“…Sheoran et al [41], Sahin et al [42], Boopathi et al [43] and Alazzawi et al [44] have all used ABC in their research. The authors obtained good results.…”

Section: Abcmentioning

confidence: 99%

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

Damia

Esnaashari

Parvizimosaed

2023

Preprint

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Software testing is the process of running an application with the goal of finding bugs and subsequently improving its quality. Software testing, as a key process, plays a role in ensuring the quality of software systems. Testing is currently considered an industry in the field of software. Given that about 40% of the cost of producing any software is spent on testing, creating tools for automatically generating test data will significantly reduce the current costs of software development. This process can be considered an optimization problem, and thus, search algorithms can be used for tackling it. The Genetic Algorithm (GA) is one of the widest algorithms in this field. In this paper, we have proposed a novel GA approach, called Group-based GA (G-GA), which differs from the standard GA algorithm in the following ways. First of all, a new fitness function has been utilized that uses search space information to guide the population. The population is divided into four groups, each of which is updated according to its fitness level. Finally, in the proposed algorithm, the selection operator has been omitted and thus, the algorithm has less complexity and calculations than the standard GA. Also, the proposed algorithm considers a good level of exploration and exploitation at each step. Experiments have shown that the proposed G-GA method, in terms of the convergence speed and the search time, significantly outperforms the basic GA, its variations, PSO, Tabu Search, and Simulated Annealing.

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“…An investigation of hybrid strategies was performed on a smaller number of programs only. M. Boopathi, et al [17] proposed a hybrid technique namely Markov chain and Artificial Bee Colony (ABC) optimization methods were used to achieve the software code coverage. A number of paths were generated using Linear-Code-Sequence-And-Jump (LCSAJ) coverage.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

Lakshminarayana¹,

Sureshkumar²

2020

Journal of Intelligent Systems

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AbstractSoftware testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).

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Quantification of Software Code Coverage Using Artificial Bee Colony Optimization Based on Markov Approach

Cited by 16 publications

References 22 publications

Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

Analysis study of the bee algorithms as a mechanism for solving combinatorial problems

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

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