一句话：提出遗传编程与线性回归的混合方法，自动推断可读的非线性方程以评估因果测试用例。
对于无法用常规手段测试的复杂软件系统，因果推理提供了一种有前景的测试思路：它借助因果模型与历史执行数据，估计并验证变量之间的因果关系。然而现有方法要么要求用户手工指定变量之间的方程关系，要么为追求自动化而采用黑箱估计、牺牲可解释性。本文提出一种遗传编程与线性回归相结合的混合方法，能够自动推断出人类可读的非线性方程，用于评估因果测试用例。实验结果表明，与单独使用遗传编程或线性回归相比，该技术通常能得到更准确的因果估计和更可靠的测试结果。
Causal reasoning is a promising and increasingly popular approach for testing complex software systems that cannot be tested using conventional approaches. This involves using a causal model and previous execution data to estimate and validate causal relationships between variables. To produce accurate causal estimates and reliable test outcomes, current approaches rely on users to specify the equational relationships between variables or sacrifice explainability for automation by using black-box estimation. In this paper, we present a hybrid between genetic programming and linear regression to automatically infer human-readable non-linear equations that can be used to evaluate causal test cases. Our results show that our technique tends to produce more accurate causal estimates and more reliable test outcomes than either technique used in isolation.