Paper Title
AN ANALYSIS OF SOFTWARE TESTING WHITE BOX & BLACK BOX
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Registration ID: IJNRD_226386
Published ID: IJNRD2408162
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Keywords
Software Testing, Genetic Algorithm, test cases, fitness function, path testing, Genetic operators.
Abstract
The fundamental objective of software testing is the development of trustworthy, error-free software. It is the goal of software testing to guarantee that the end result satisfies the user's needs. Because of this, software quality and efficiency have seen huge boosts. Software testing is time-consuming and labor-intensive, but it's necessary to make sure the system works as it should. Therefore, it is a tedious and time-consuming task to verify that the system follows the rules. Substituting automation testing for manual testing is one way to overcome the problem of limited testing time. The genetic algorithm is just one of many tactics that have their roots in biological evolution. This approach is based on two pillars: the interpretation and genetic operators. It works well for figuring out if a beginning population is suitable. Because of its importance in genetic algorithms, the fitness function should be strong. Reading up on fitness functions, time, cost, and path coverage revealed that our understanding was weak. To make up for these flaws, scientists have developed a new fitness function using three algorithms and population fitness formulas. The first objective of developing the algorithm was to create a parser capable of counting the instances of iterative and recursive variables within the code. An other method known as GABVIE (Genetic technology based variable importance evaluation) can determine the optimal action by assigning relative values to the code variables. The code demands that the algorithm that has been built accept inputs of different frequencies. The recently constructed fitness function has established the significance of the frequencies of the variables defined by the parser. In order to avoid early convergence and expand the search area, the third technique, 'GABVOCOMO' (Genetic method Based Variation of crossover and mutation operator), uses every possible combination of the genetic algorithm's operators—crossover and mutation. In order to mitigate the total delay brought about by the genetic algorithm's early convergence, this method adjusts to accommodate a diverse population.
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How To Cite (APA)
HEENA SAINI , SUDHIR KUMAR VERMA , & NAVIN KUMAR TYAGI (August-2024). AN ANALYSIS OF SOFTWARE TESTING WHITE BOX & BLACK BOX. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), b684-b692. https://ijnrd.org/papers/IJNRD2408162.pdf
Issue
Volume 9 Issue 8, August-2024
Pages : b684-b692
Other Publication Details
Paper Reg. ID: IJNRD_226386
Published Paper Id: IJNRD2408162
Research Area: Science & Technology
Author Type: Indian Author
Country: Bulandshahar, U.P, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2408162.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408162
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