Quality-Optimised Differencing Algorithms for Models

模型质量优化的差分算法

• 批准号: 186192750
• 负责人: Professor Dr. Udo Kelter
• 金额: --
• 依托单位: Lehrstuhl Softwaretechnik und Datenbanksysteme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/186192750?language=en
• 关键词: Quality; Optimised; Differencing; Algorithms; Models

In modern model-based development processes, models are part of the operating software. They assume the role of source code and they are, like source code, versioned frequently. Consequently, powerful tools for comparing, merging, and patching models are required. Such tools are based on algorithms which calculate the difference between models. Differencing algorithms for models must take the structure of each model type and the characteristics of typical edit operations into account. They should explain the difference between models as understandably as possible, in other words, produce differences of high quality. However, improving the quality of differences deteriorates the already high response times, so one has to compromise between quality and cost. Systematic approaches to defining the quality of differences are currently missing. This project thus aims at defining notions and measures for the quality of model differences and at optimizing differencing algorithms both in terms of runtime as well as the achieved quality of the differences. In the first period of funding, the project has developed methods and tools for creating benchmarks which can be used to evaluate differencing algorithms. A substantial set of test data, mainly class diagrams, was created and various variants of algorithms have been evaluated using these test models. In the meantime, new differencing algorithms became available which enhance the quality of differences by using refactorings and other complex operations to report changes. On the basis of the preliminary results the third project year is planned to develop a new conceptual framework which distinguishes variants of the quality of differences depending on the requirements of various classes of differencing tools. It is planned to re-evaluate differencing algorithms for class diagrams and to evaluate for the first time algorithms for differencing algorithms for BPMN models, which show significantly different characteristics and for which many refactorings have been proposed.

在现代基于模型的开发过程中，模型是操作软件的一部分。它们承担了源代码的角色，并且像源代码一样，经常进行版本控制。 因此，需要强大的工具来比较、合并和修补模型。 这些工具基于计算模型之间差异的算法。 模型的差分算法必须考虑每种模型类型的结构和典型编辑操作的特征。他们应该尽可能地解释模型之间的差异，换句话说，产生高质量的差异。然而，提高差异的质量会使已经很高的响应时间恶化，因此必须在质量和成本之间做出妥协。目前缺乏界定差异质量的系统方法。因此，该项目的目的是定义概念和措施的质量模型差异和优化差异算法的运行时间以及实现的差异质量。 在第一阶段的资金，该项目已开发的方法和工具，创建可用于评估差分算法的基准。大量的测试数据，主要是类图，创建和各种变体的算法已经使用这些测试模型进行了评估。与此同时，新的差分算法变得可用，通过使用重构和其他复杂的操作来报告变化，从而提高了差异的质量。 在初步结果的基础上，计划在第三个项目年制定一个新的概念框架，根据各类差异工具的要求区分差异质量的变体。计划重新评估类图的差分算法，并首次评估BPMN模型的差分算法，这些模型显示出显着不同的特性，并提出了许多重构。