一句话：提出 Baital，利用基于文字加权函数的自适应加权采样生成高 t-wise 覆盖率的测试集，显著优于均匀采样。
高度可配置复杂软件的普及要求高效的测试方法，t-wise 覆盖率是衡量测试套件及测试生成引擎质量的重要指标。在配置集合受约束的情况下，基于均匀采样的测试生成虽被广泛视为当前先进方法，却常难以达到高 t-wise 覆盖率。本文提出 Baital，一种基于文字加权函数（literal weighted functions）的自适应加权采样方法，用于生成具有高 t-wise 覆盖率的测试集。作者展示该方法取得的 t-wise 覆盖率显著高于均匀采样。文中指出，文字加权采样的这种新用法为未来实证与理论研究开辟了若干有趣方向。
The rise of highly configurable complex software and its widespread usage requires design of efficient testing methodology. t-wise coverage is a leading metric to measure the quality of the testing suite and the underlying test generation engine. While uniform sampling-based test generation is widely believed to be the state of the art approach to achieve t-wise coverage in presence of constraints on the set of configurations, such a scheme often fails to achieve high t-wise coverage in presence of complex constraints. In this work, we propose a novel approach Baital, based on adaptive weighted sampling using literal weighted functions, to generate test sets with high t-wise coverage. We demonstrate that our approach reaches significantly higher t-wise coverage than uniform sampling. The novel usage of literal weighted sampling leaves open several interesting directions, empirical as well as theoretical, for future research.