Evaluation of Software Testing Methodologies in Cloud Computing Environment

Abstract

Cloud computing has transformed software development and testing by offering scalable, cost-efficient, and on-demand infrastructure, leading to a transition from conventional testing methods to cloud-native practices. Notwithstanding these benefits, cloud-based testing encounters issues related to performance, security, and automated integration, which may compromise software reliability and efficiency. Prior studies have investigated elements such adaptive fault tolerance, server-less testing, AI-driven automation, and simulation tools; nevertheless, a holistic framework that encompasses efficiency, security, and resource optimization is still inadequately developed. This paper aims to assess current software testing approaches in cloud computing environments and offer an improved automated framework that improves efficiency, reduces human effort, and boosts security. A mixed-method research methodology was employed, incorporating surveys of 250 participants comprising QA managers, developers, DevOps engineers, and testers, with experimental validation utilizing a Docker-based PyTest automated testing framework. Data were examined utilizing descriptive statistics, chi-square tests, ANOVA, and independent t-tests to evaluate the efficacy of diverse testing procedures and instruments. Findings demonstrate that automation, namely the PyTest-Docker architecture, markedly enhances performance, security, and cost efficiency, while decreasing the workforce from 4–5 testers to 1–2. Log-based performance monitoring and security simulations validated reliability and fault detection capabilities. The research indicates that the use of AI-driven automation, predictive analytics, and containerized modular testing fosters a robust, efficient, and sustainable cloud testing ecosystem. The results are crucial for directing software quality assurance methodologies, enhancing resource allocation, and influencing forthcoming cloud-native testing frameworks.