To identify defects and security risks in many real-world applications structured test cases, including test cases structured as trees are required. A simple approach is to generate random trees as test cases [random testing (RT)]; however, the RT approach is not very effective. In this work, we investigate and extend the black-box tree test case generation approaches. We introduce a novel model to produce superior test case generation based around the idea of measuring the diversity of a tree test set. This initial approach is further extended by adding a second model which describes the distribution of tree sizes. Both models are realized via a multi-objective optimization algorithm. An empirical study is performed with four real-world programs indicating that the generated tree test cases outperform test cases generated by other methods.

中文翻译：

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通过多样性生成黑盒树测试用例

为了识别许多实际应用程序结构化测试用例中的缺陷和安全风险，包括需要以树结构化的测试用例。一个简单的方法是生成随机树作为测试用例[随机测试（RT）]；但是，RT 方法不是很有效。在这项工作中，我们研究并扩展了黑盒树测试用例生成方法。我们引入了一种新颖的模型，以基于测量树测试集多样性的思想来生成卓越的测试用例。通过添加描述树大小分布的第二个模型，进一步扩展了这种初始方法。两种模型都是通过多目标优化算法实现的。对四个真实世界的程序进行了实证研究，表明生成的树测试用例优于其他方法生成的测试用例。