Development of a Programmed Generation of t-way Test cases Using an Improved Particle Swarm Optimization Strategy

Prasad, M. Lakshmi; Sastry, J. K. R.; Mallikarjuna, Basetty; Sitaramulu, V.; Srinivasulu, Ch.; Naib, Bharat Bhushan

doi:10.1109/icacite53722.2022.9823783

Cited by 6 publications

(1 citation statement)

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“…These methods offer distinct perspectives that guide the search process for optimal solutions. Based on a comprehensive review of recent research on diverse search-based optimal control methodologies, Prasad et al [22] introduce a swarmsmart exploring technique aimed at generating test records that are relatively close in nature. The proposed approach seeks to enhance the effectiveness of the search process and promote the generation of diverse and comprehensive test cases.…”

Section: The Pso Algorithmmentioning

confidence: 99%

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

Li,

Song,

Zhang

2024

Applied Sciences

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In applications of software testing, the cause–effect graph method is an approach often used to design test cases by analyzing various combinations of inputs with a graphical approach. However, not all inputs have equal impacts on the results, and approaches based on exhaustive testing are generally time-consuming and laborious. As a statute-based software-testing method, combinatorial testing aims to select a small but effective number of test cases from the large space of all possible combinations of the input values for the software to be tested, and to generate a set of test cases with a high degree of coverage and high error detection capability. In this paper, the reduced ordered binary decision diagram is utilized to simplify the cause–effect graph so as to reduce the numbers of both the inputs and test cases, thereby saving the testing cost. In addition, an improved particle swarm optimization algorithm is proposed to significantly reduce the computation time needed to generate test cases. Experiments on several systems show that the proposed method can generate excellent results for test case generation.

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Section: The Pso Algorithmmentioning

confidence: 99%