Use of Clonal Selection Algorithm as Software Test Data Generation Technique

Pachauri, Ankur; Gursaran,

doi:10.1109/acct.2012.118

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The results show that the projected approach has high path coverage with minimum number of generations. Ankit Pachauri and Gursaran [36] has projected test data generation approach based on Clonal selection algorithm. They have used AI and NBD Approximation level with normalized branch distance as objective function to validate the test data.…”

Section: Related Work On Test Data Generationmentioning

confidence: 99%

“…[16] The clonal selection theory credited to Burnet was proposed to account for the behaviour and capabilities of antibodies in the acquired immune system. [44] The theory suggests that starting with the initial repertoire of general immune cells, the system is able to change itself the CSA was designs as a general machine learning approach and has been applied to pattern recognition, functional optimization , combinational optimization and test data generation domain [36]. The Pseudo code of Clonal algorithm is as follows:…”

Section: Negative Selection Algorithmmentioning

confidence: 99%

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Systematic Literature Review in Software Test Data Generation

Kumar¹,

Chopra²,

Gupta³

2023

Emerging Trends in Engineering and Management

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In software development, software testing is an important practice which comprises of different activities. It is the time consuming and cost oriented process. In testing, it is very important to select test data generation process wisely because testing efficiency is highly dependent on the data used and it may affect the cost and time. Soft computing algorithms explore test data in search-based software testing to optimize the coverage metric, which can be called an optimization challenge. Some Meta-Heuristics algorithms (Artificial Bee Colony, Particle Swarm Optimization, Genetic Algorithm, Firefly Algorithm and Ant Colony Optimization Algorithms) are selected in this paper for comparative study along with Artificial Immune Algorithms (Negative Selection Algorithm, Clonal selection, and Hybrid Negative Selection Algorithm). The Immune algorithm also have significant impact in engineering applications and in the field of software test data generation. A survey on automated test data generation has been done on the various criteria such as type of objective function use, type and number of experiments performed for specific technique, comparison with other techniques, types of parameters used and the performance of the algorithm. From this survey it has been observed that the immune algorithms outperform meta-heuristic algorithms in terms of average coverage, average generation, cost, and average test data generated. But somehow the number of comparisons to generate test data in immune algorithms is more than the Metaheuristic algorithms.

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Section: Related Work On Test Data Generationmentioning

confidence: 99%

Section: Negative Selection Algorithmmentioning

confidence: 99%

Systematic Literature Review in Software Test Data Generation

Kumar¹,

Chopra²,

Gupta³

2023

Emerging Trends in Engineering and Management

View full text Add to dashboard Cite

show abstract

“…The branch distance calculates how close the test case was to stay on a path leading to the target. The branch distance is computed according to Equation (1) [23].…”

Section: Fitness Functionmentioning

confidence: 99%

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020

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Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

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“…Despite search-based algorithms, artificial immune algorithms are also used for data generations, which significantly improves searchbased algorithms [14,15]. The negative Selection algorithm [16,17] colonel selection algorithm [18] is being also applied in test data generation. A hybrid approach based on the artificial immune algorithm NSA and metaheuristic algorithm PSO is also proposed for test data generation [19].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach for Automated Test Data Generation

Kumar

Chopra

2022

JICTS

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Software testing has long been thought to be a good technique to improve the software quality and reliability. Path testing is the most reliable software testing technique and the key method for improving software quality among all testing approaches. On the other hand, test data quality has a big impact on the software testing activity’s ability to detect errors or defects. To solving testing problem, one must locate the entire search space for the relevant input data to encompass the different paths in the testable program. To satisfy path coverage, it is vital test to look at the accumulated test data across the thorough search area. A new approach based on ant colony optimization and negative selection algorithm (HACO-NSA) is presented in this research which overcome the flaws associated with search-based test data by generated automated test data. The optimum path testing objective is to generate appropriate test data to maximise coverage and to enhance the test data’s efficacy, as a result, the test data’s adequacy is validated using a path-based fitness function. In the NSA generation stage, the suggested method alters the new detectors creation using ACO. The proposed approach is evaluated for metrics such as average coverage, average generation, average time, and success rate and comparison has been done with random testing, ant colony optimization and negative selection algorithm Different benchmark programs have been used for object-oriented system. The findings show that the hybrid methodology escalates the coverage percentage and curtail test data size, reduces the redundancy in data and enhances the efficiency. The proposed approach is follows IEEE 829-2008 test documentation in entire testing process.

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Use of Clonal Selection Algorithm as Software Test Data Generation Technique

Cited by 7 publications

References 14 publications

Systematic Literature Review in Software Test Data Generation

Systematic Literature Review in Software Test Data Generation

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Hybrid Approach for Automated Test Data Generation

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