DJPlus：为图模型中的强覆盖准则生成最小测试套件
DJPlus: Generating minimal test suites for strong coverage criteria in graph models
浏览论文内容
中文总结 AI 辅助
DJPlus提出一种优化驱动的测试生成方法，在满足图覆盖需求的同时生成精简测试套件，通过减少冗余测试步骤显著缩短执行时间，并在四个真实系统上验证了强覆盖准则的可行性。
中文摘要 AI 辅助
从图模型自动生成测试对于基于模型的测试至关重要。在这类测试中，图覆盖确保了测试套件的强度，但也导致测试用例过长，从而在待测系统上执行时耗时较多。我们提出了一种新颖的优化驱动方法DJPlus，该方法在满足给定基于图的测试需求的同时生成精简的测试套件。我们实现了DJPlus，并在四个真实系统上展示了边对准则（一种比顶点或边准则更强的覆盖准则）的可行性，而主路径准则则存在可扩展性问题。我们的评估表明，替代方法生成的冗余测试步骤比DJPlus多2到26倍，且DJPlus通过减少测试步骤数量缩短了测试执行时间。这些结果表明，DJPlus是朝着应对工业规模下基于模型的测试挑战迈出的积极一步。
英文摘要
Automated test generation from graph models is essential to model-based testing. In this type of testing, graph coverage ensures test suite strength but also results in long test cases that take time to execute on the system under test. We propose a novel optimization-driven method, DJPlus, which generates reduced test suites while satisfying given graph-based test requirements. We implement DJPlus and show the feasibility of edge-pair criterion, a stronger coverage criterion than vertex or edge criteria, on four realistic systems, while prime path criterion poses scalability issues. Our evaluation reveals that the alternative methods generate 2 to 26 times more redundant test steps than DJPlus and DJPlus decreases test execution times by reducing the number of test steps. These results show that DJPlus is a positive step towards tackling the challenges of model-based testing at an industrial scale.
发表机构
- İstanbul Technical University（伊斯坦布尔理工大学）
- University of Antwerp（安特卫普大学）
- Graz University of Technology（格拉茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。
