Optimization of Test Case Generation using Genetic Algorithm (GA)

📝 Abstract

Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with software testing has quite a few underlying concerns, which are very important and need to pay attention on these issues. These issues are effectively generating, prioritization of test cases, etc. These issues can be overcome by paying attention and focus. Solitary of the greatest Problems in the software testing area is usually how to acquire a great proper set associated with cases to confirm software. Some other strategies and also methodologies are proposed pertaining to shipping care of most of these issues. Genetic Algorithm (GA) belongs to evolutionary algorithms. Evolutionary algorithms have a significant role in the automatic test generation and many researchers are focusing on it. In this study explored software testing related issues by using the GA approach. In addition to right after applying some analysis, better solution produced, that is feasible and reliable. The particular research presents the implementation of GAs because of its generation of optimized test cases. Along these lines, this paper gives proficient system for the optimization of test case generation using genetic algorithm.

💡 Analysis

Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with software testing has quite a few underlying concerns, which are very important and need to pay attention on these issues. These issues are effectively generating, prioritization of test cases, etc. These issues can be overcome by paying attention and focus. Solitary of the greatest Problems in the software testing area is usually how to acquire a great proper set associated with cases to confirm software. Some other strategies and also methodologies are proposed pertaining to shipping care of most of these issues. Genetic Algorithm (GA) belongs to evolutionary algorithms. Evolutionary algorithms have a significant role in the automatic test generation and many researchers are focusing on it. In this study explored software testing related issues by using the GA approach. In addition to right after applying some analysis, better solution produced, that is feasible and reliable. The particular research presents the implementation of GAs because of its generation of optimized test cases. Along these lines, this paper gives proficient system for the optimization of test case generation using genetic algorithm.

📄 Content

International Journal of Computer Applications (0975 – 8887) Volume 151 – No.7, October 2016 6 Optimization of Test Case Generation using Genetic Algorithm (GA) Ahmed Mateen Department of Computer Science, University of Agriculture Faisalabad, Pakistan

Marriam Nazir Department of Computer Science, University of Agriculture Faisalabad, Pakistan

Salman Afsar Awan, PhD Department of Computer Science, University of Agriculture Faisalabad, Pakistan ABSTRACT Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with software testing has quite a few underlying concerns, which are very important and need to pay attention on these issues. These issues are effectively generating, prioritization of test cases, etc. These issues can be overcome by paying attention and focus.
Solitary of the greatest Problems in the software testing area is usually how to acquire a great proper set associated with cases to confirm software. Some other strategies and also methodologies are proposed pertaining to shipping care of most of these issues. Genetic Algorithm (GA) belongs to evolutionary algorithms. Evolutionary algorithms have a significant role in the automatic test generation and many researchers are focusing on it. In this study explored software testing related issues by using the GA approach. In addition to right after applying some analysis, better solution produced, that is feasible and reliable. The particular research presents the implementation of GAs because of its generation of optimized test cases. Along these lines, this paper gives proficient system for the optimization of test case generation using genetic algorithm. Keywords Optimization, Genetic Algorithm, Test case, Generation, Design, Testing.

1. INTRODUCTION Computer software assessment is one of the majorities of labor strenuous as well as pricey period with the software program improvement lifetime routine. Computer software assessment consists of the test circumstance, age group as well as test suite optimization that includes a strong impact on the particular usefulness as well as productivity connected with software program assessment. In the last number of ages, there was an energetic investigation to automate the task connected with the test circumstance, age group, however the tribes are already confined by the dimension and the complexity connected with the software program. The high quality software must satisfy the user requirements and customer demands. Satisfaction of customer has always been important because it indicates that business people have to manage and improve their product. Once testing involving software is actually a good important process connected with assessing the software to help distinguish it is quality.
   It is an important area of the software engineering. Modern software systems are extremely reliable in addition to correct. Represented an optimization tool where their aim is to find a problem solution to a given problem. Based on inheritance, natural selection, mutation, and sexual reproduction, they try to give after many generations the optimal solution in a finite time [1]. Testing techniques are test case design method. Test cases are developed using various testing techniques to achieve more effective testing of application [2]. Genetic algorithms are generally as outlined by evolutionary ideas connected with natural menu in addition to genetics. Genetic algorithms solve the current problems step via step as well as provide and then generation [3]. Testing is a basic activity of the item headway handles, and robotic test period adds to reduce cost and time trials. The ideal test suites are conceived by the strategy for examining measurements. Testing is the most essential stage in the item change life cycle. The testing stage is the last channel for all oversights of rejection and commission. Testing writing computer programs are altogether more eccentric than rehearsing a framework to check whether it works. Each study, audit, survey, walks around, social event code sees, all is when in doubt a kind of test. The more fruitful that can make early is static attempting, the less issues involved in the dynamic periods of testing. IT has shown again and again that the earlier a defect recognized and cleared, the lower the additional change cost associated with ousting the mix-up. Plan for testing begin when each item thing is portrayed. All evolutionary algorithms similar to Genetic Algorithm acquire near optimal solution.
   The essential objective of testing is to exhibit that the item thing at any rate, meets a course of action of pre-set up affirmation criteria under a suggested set of environmental circumstances. The

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