大多数自动化软件测试任务可以从测试用例的抽象表示中受益。传统上，这是通过基于测试案例的代码覆盖范围来完成的。规范级别的标准可以替换代码覆盖范围以更好地表示测试用例的行为，但通常不具有成本效益。在本文中，我们假设测试用例的执行痕迹可以使其在自动测试任务中抽象其行为的好选择。我们提出了一种新颖的嵌入方法Test2VEC，该方法将测试执行映射到潜在空间。我们在测试案例的优先级（TP）任务中评估了此表示形式。我们的默认TP方法基于嵌入式向量与历史失败测试向量的相似性。我们还根据测试向量的多样性研究了一种替代方案。最后，我们提出了一种决定给定测试套件的方法，以决定选择哪种TP。该实验基于几个真实和种子故障，具有超过一百万个执行痕迹。结果表明，就第一个失败测试案例（FFR）的中位数等级而言，我们提议的TP将最佳替代品提高了41.80％。就中位数APFD和中位数归一化FFR而言，它的表现优于传统代码覆盖范围的方法25.05％和59.25％。

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.