用于自动生成一致领域特定模型的图求解器

Oszkár Semeráth, András Szabolcs Nagy, Dániel Varró
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引用次数: 43

摘要

软件和系统工程中的许多测试和基准测试场景依赖于图形模型的系统生成。例如,安全标准所必需的工具鉴定将需要特定于某个领域的大量一致的(格式良好或格式错误的)实例模型。然而,自动生成符合元模型并满足工业领域所有格式良好约束的一致性图模型是一个重大挑战。将图形模型映射到一阶逻辑规范以使用后端逻辑求解器(如Alloy或Z3)的现有解决方案存在严重的可伸缩性问题。在本文中,我们提出了一个图求解器框架,用于自动生成一致的特定领域实例模型,该模型通过结合部分模型的细化、形状分析、增量图查询评估和基于规则的设计空间探索等先进技术,直接对图进行操作,以提供更有效的指导。我们在四个领域进行的初步性能评估表明,与使用Alloy的基于映射的方法相比,我们的方法能够生成大1-2个数量级的模型(500到6000个对象!)。
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A Graph Solver for the Automated Generation of Consistent Domain-Specific Models
Many testing and benchmarking scenarios in software and systems engineering depend on the systematic generation of graph models. For instance, tool qualification necessitated by safety standards would require a large set of consistent (well-formed or malformed) instance models specific to a domain. However, automatically generating consistent graph models which comply with a metamodel and satisfy all well-formedness constraints of industrial domains is a significant challenge. Existing solutions which map graph models into first-order logic specification to use back-end logic solvers (like Alloy or Z3) have severe scalability issues. In the paper, we propose a graph solver framework for the automated generation of consistent domain-specific instance models which operates directly over graphs by combining advanced techniques such as refinement of partial models, shape analysis, incremental graph query evaluation, and rule-based design space exploration to provide a more efficient guidance. Our initial performance evaluation carried out in four domains demonstrates that our approach is able to generate models which are 1-2 orders of magnitude larger (with 500 to 6000 objects!) compared to mapping-based approaches natively using Alloy.
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