带约束的最优组合测试：平衡行为
Optimal Combinatorial Testing with Constraints: The Balancing Act
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中文总结 AI 辅助
研究带约束的最优组合测试问题。提出基于整数规划的精确算法和更快启发式算法，用于解决开关按钮配置测试难题，性能优于或堪比基线，展现了在不同约束条件下组合测试的特性及算法优势。
中文摘要 AI 辅助
想象面对有多个开关按钮的驾驶舱，全面测试需尝试大量配置。实际中多数错误源于少数组件交互，覆盖所有可能的两两配置测试是个好开端。但一旦某些两两配置被禁止，问题就从简单变为NP难。本文重新审视二元参数上具有两两覆盖的无约束组合测试，并与受约束情况对比，展示并推测属性变化。特别讨论为最小化测试，各按钮开和关次数尽量接近的程度。我们提出基于整数规划的首个精确算法和更快的启发式算法，二者性能优于或堪比基线。
英文摘要
Imagine that you are in front of a cockpit with several on-off buttons. If you were to thoroughly test it, you would need to try a prohibitive number of configurations. But since most bugs in practice can be isolated to interactions among few components, having tests that cover every possible pairwise configuration is a good start. However, this is a problem that goes from easy to NP-hard as soon as some pairwise configurations are forbidden. In this paper, we revisit unconstrained combinatorial testing with pairwise coverage on binary parameters and contrast it with the constrained case, showing and conjecturing properties that either are upheld or change from one to the other. In particular, we discuss the extent to which it remains a good idea -- and sometimes indeed optimal -- to have every button almost as many times on as off to minimize testing. We propose the first exact algorithm based on integer programming and a faster heuristic that often produces optimal solutions, both outperforming or competitive with their baselines.
