Unit testing is critical to the software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with genetic algorithms by specifying the coverage criterion (e.g., branch coverage). However, a good test suite must have different properties, which cannot be captured using an individual coverage criterion. Therefore, the state-of-the-art approach combines multiple criteria to generate test cases. Since combining multiple coverage criteria brings multiple objectives for optimization, it hurts the test suites' coverage for certain criteria compared with using the single criterion. To cope with this problem, we propose a novel approach named \textbf{smart selection}. Based on the coverage correlations among criteria and the subsumption relationships among coverage goals, smart selection selects a subset of coverage goals to reduce the number of optimization objectives and avoid missing any properties of all criteria. We conduct experiments to evaluate smart selection on $400$ Java classes with three state-of-the-art genetic algorithms under the $2$-minute budget. On average, smart selection outperforms combining all goals on $65.1\%$ of the classes having significant differences between the two approaches. Secondly, we conduct experiments to verify our assumptions about coverage criteria relationships. Furthermore, we experiment with different budgets of $5$, $8$, and $10$ minutes, confirming the advantage of smart selection over combining all goals.

翻译：单元测试是软件开发过程中的关键环节，用于确保程序中基本编程单元（如方法）的正确性。基于搜索的软件测试（SBST）是一种自动化生成测试用例的方法，通过指定覆盖准则（如分支覆盖率），利用遗传算法生成测试用例。然而，一个优秀的测试套件需要具备多种特性，而单一的覆盖准则无法全面捕捉这些特性。因此，现有最先进的方法通过组合多个准则来生成测试用例。由于组合多个覆盖准则会引入多个优化目标，与使用单一准则相比，这可能会降低测试套件在特定准则上的覆盖率。为解决这一问题，我们提出了一种名为**smart selection**的新方法。基于准则间的覆盖相关性以及覆盖目标间的包含关系，smart selection选择部分覆盖目标以减少优化目标数量，同时避免遗漏任何准则的特性。我们在$400$个Java类上进行了实验，在$2$分钟预算下使用三种最先进的遗传算法评估smart selection的性能。平均而言，在两种方法存在显著差异的类别中，smart selection在$65.1\%$的类别上优于组合所有目标的方法。其次，我们通过实验验证了关于覆盖准则关系的假设。此外，我们还在$5$、$8$和$10$分钟的不同预算下进行了实验，进一步证实了smart selection相对于组合所有目标的优势。