Reviewing Software Testing Models and Optimization Techniques: An Analysis of Efficiency and Advancement Needs

Abstract

Software testing is a crucial component of software engineering that aims to confirm the intended functionality of software modules and minimize the likelihood of future failures. This paper provides a comprehensive review of various software testing models and optimization techniques available in the literature, emphasizing their performance analysis and related research papers. The paper analyzes and discusses the most commonly used software testing models, including waterfall, incremental, V-model, agile, and spiral models, and identifies several areas for improvement to increase their effectiveness. These areas include using machine learning techniques to automate and optimize testing processes, reducing the number of test cases required, and introducing new metrics to gauge the success of testing. Moreover, the paper suggests developing entirely novel methods to deal with the challenges of contemporary software programs, such as the Internet of Things and artificial intelligence. This paper aims to analyze various software testing models and optimization techniques thoroughly, highlight their advantages and disadvantages, and suggest improvements to increase their efficiency and effectiveness. By continuously improving and optimizing software testing processes, software modules can function as intended, minimizing the likelihood of future failures.