In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios.

在本文中，我们提出了一种自动合成一致、多样和结构逼真的新技术特定领域的图模型。图模型是（1）一致的，如果它是元模型兼容的并且满足域的格式良好的约束；(2) 如果节点的局部邻域高度不同，则它是多样的；(1) 根据网络科学、数据库或软件工程中使用的各种图度量，如果合成图与代表性真实模型的距离很近，那么它在结构上是现实的。我们的方法通过模型扩展操作符使用爬山策略来增长模型，其方式是 (A) 确保模型中没有约束违规（出于一致性原因），而 (B) 选择更现实的候选者以最小化目标度量标准值（代表实际模型）。我们评估了使用多个指标作为指导启发式生成真实模型的方法的有效性，并在具有大量真实人体模型的三个案例研究的背景下与其他模型生成器进行了比较。我们还强调，我们的技术能够生成多种模型，这是许多测试场景的要求。