Graph-Centric Approaches for Coverage Optimization in Software Requirement Testing

In software testing, traceability links between software requirements and test cases are crucial for managing test coverage and detecting defects effectively. Accurate traceability enables comprehensive coverage analysis, identification of untested requirements, and targeted defect detection. The manual effort required to establish and update traceability links often leads to high labor costs and a greater risk of human error. Furthermore, as requirements evolve during the development lifecycle, the effort needed to maintain accurate links increases, driving up maintenance costs and further complicating test management.This study proposes a graph-based approach to establish and maintain traceability throughout the software testing process. By comparing various models for their ability to identify semantic relationships between software requirements and test cases, we selected the most effective method to create accurate traceability links. These links are further utilized through graph queries, enabling efficient analysis of test coverage, identification of untested requirements, and discovery of high-similarity requirement clusters, thereby enhancing the overall testing process. Automatically generating test cases based on query results, our approach seamlessly integrates into the software testing lifecycle, enhancing both coverage and efficiency. In a case study involving real-world industrial data, we effectively identified previously untested requirements and generated a substantial number of high-quality test cases. The results validate the applicability and effectiveness of our approach, demonstrating its potential to improve test traceability and reliability in practical software development environments.