Learning from examples to improve Automation in Model-Driven Engineering

从示例中学习以提高模型驱动工程的自动化

• 批准号: RGPIN-2014-06702
• 负责人: Sahraoui, Houari
• 金额: $ 3.35万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567978
• 关键词: Learning; examples; improve; Automation; Model

The model-driven engineering (MDE) received much attention in recent years due to its promise to reduce the complexity of the development and maintenance of software applications. However, and notwithstanding the success stories reported in the past decade, MDE is still at the early stages of adoption. One major obstacle to the adoption of MDE is the difficulty to automate many activities of this development paradigm. Automation is a keystone and a founding principle of MDE. In this paradigm, domain-specific modeling languages are combined with transformation engines and generators to produce various software artifacts. Defining modeling languages, writing transformations, and maintaining consistency between the involved models and other artifacts are typical tasks that are difficult to automate. This difficulty comes mainly from the lack of knowledge in some specific domains. This is essentially the case for the definition of meta-models and transformation mechanisms. Difficulty of automation can also be related to various inconsistencies introduced by some manual tasks as in the case of model maintenance. To help in the improvement of automation in MDE, we propose to use examples of artifacts that define the inputs and outputs of the task to automate. Depending on this task, examples could take different forms. For model transformation or refactoring, for instance, examples are pairs of source and target models. Similarly, in metamodel definition, examples could be models that are labeled as valid/invalid. The automation of a task is viewed as an optimization process that derives the automation knowledge that best conforms to the examples at hand. Depending on the size of the problem, i.e., search space, such a process could use an exhaustive or heuristic search. The above-mentioned idea will be explored for different MDE tasks. These tasks include model transformation, model refactoring, transformation specification, and precise metamodel definition.

近年来，模型驱动工程(MDE)因其能够降低软件应用程序开发和维护的复杂性而备受关注。然而，尽管在过去十年中报道了一些成功的案例，MDE仍处于采用的早期阶段。采用MDE的一个主要障碍是难以将这种开发范例的许多活动自动化。自动化是现代教育的基石和基本原则。在这个范例中，领域特定的建模语言与转换引擎和生成器相结合，以产生各种软件构件。定义建模语言、编写转换以及维护所涉及的模型和其他构件之间的一致性是很难实现自动化的典型任务。这一困难主要来自于缺乏某些特定领域的知识。对于元模型和转换机制的定义，基本上就是这种情况。自动化的困难也可能与一些手工任务引入的各种不一致有关，例如在模型维护的情况下。为了帮助改进MDE中的自动化，我们建议使用定义要自动化的任务的输入和输出的构件的示例。根据任务的不同，示例可能会采取不同的形式。例如，对于模型转换或重构，示例是源模型和目标模型对。类似地，在元模型定义中，示例可以是标记为有效/无效的模型。任务的自动化被认为是一个优化过程，它派生出最符合手头示例的自动化知识。根据问题的大小，即搜索空间，这样的过程可以使用穷举或启发式搜索。上述想法将针对不同的MDE任务进行探索。这些任务包括模型转换、模型重构、转换规范和精确的元模型定义。