Optimization of Test Case Generation using Genetic Algorithm (GA)

TL;DR

This paper presents a genetic algorithm-based method to optimize test case generation, aiming to improve software testing efficiency and effectiveness by producing reliable and feasible test sets.

Contribution

It introduces a novel application of genetic algorithms for optimizing test case generation in software testing.

Findings

Generated test cases are more optimized and reliable.

The approach improves testing efficiency.

The system effectively addresses test case prioritization issues.

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.