Test case model for maintaining software under the concept of risk and requirement-based prioritization

Abstract

Test case growth in software maintenance poses multifaceted challenges, including extended execution times, heightened costs, maintenance complexities, and increased system intricacy. Larger test cases not only introduce redundancy but also elevate the risk of overlooking defects during testing, emphasizing the need to manage test case expansion effectively while maintaining adequate testing coverage. One approach to mitigating this issue is risk-based prioritization, which focuses on testing critical and high-risk areas of the software. By emphasizing these pivotal aspects, this method aims to streamline test cases, optimizing testing efforts. However, it has limitations, as it may not comprehensively address all functional requirements, necessitating an in-depth understanding of the software system's architecture and vulnerabilities. Furthermore, requirements-based prioritization ensures thorough testing of all functional requirements, crucial for meeting industry standards and regulatory compliance. This approach utilizes clear criteria provided by requirements to evaluate whether software functions as intended. While effective in guaranteeing comprehensive coverage, it may fall short in addressing all potential risks and vulnerabilities, potentially undermining its effectiveness in identifying and prioritizing critical areas of the software. Considering these challenges, a nuanced approach that combines risk-based and requirements-based prioritization could offer a balanced solution. By leveraging the strengths of both methods, it becomes possible to address the diverse aspects of software testing, optimizing coverage, and efficiently managing test case growth. Moreover, the proposed model demonstrates promising results in tackling these challenges. With a notable test suite size reduction of 0.61-1.05% compared to traditional methods and a faultless percentage surpassing comparative studies by 0.05-0.75% across seven C-language System Under Test (SUT) programs, the model showcases its efficacy in improving testing efficiency and reliability. These findings underscore the potential of adopting advanced models to navigate the complexities associated with test case management and software testing in general.