Integrating the analysis of multiple non-functional properties in the model-driven software engineering process

在模型驱动的软件工程过程中集成多个非功能属性的分析

• 批准号: RGPIN-2015-05225
• 负责人: Petriu, Dorina
• 金额: $ 1.75万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662049
• 关键词: Integrating; analysis; multiple; non; functional

This proposal is about seamless integration of multiple formal analysis models for Non-Functional Properties (NFPs), such as performance, reliability, availability, fault-tolerance, scalability, security, maintainability, cost, etc., into the Model-Driven Engineering (MDE) process, in order to guide the design choices from an early stage and to insure that the system under construction will meet its non-functional requirements. Such analysis models (also known as quality models) are based on existing formalisms and tools (e.g., queueing networks, stochastic Petri nets, stochastic process algebras, Markov chains, fault trees, probabilistic time automata, etc.) and are automatically derived by model transformations from the software models built for development. Developing software systems that exhibit a good trade-off between multiple NFPs is hard because the design of the software under construction and its underlying platforms have a large number of degrees of freedom spanning a very large discontinuous design space that cannot be exhaustively explored. Another challenge is due to the fact that some NFPs are conflicting (for example, security and performance).***The long-term objective of the proposed research program is to add more "engineering" to model-driven software engineering by supporting seamless integration of the analysis of multiple NFPs into the MDE process. Different NFP analysis models based on appropriate existing formalisms can be automatically derived by model transformations from the software models built for development, as explored in previous research. The software and NFP analysis models must co-evolve together. An important research effort will go into investigating how multiple NFP analysis models can be used to find a good (preferably optimal) design solution, in which all non-functional requirements are met. Another important aspect of the proposed research is concerned with (semi)automating the software process tasks/activities related to NFP analysis, asking for human intervention only when the designers need to provide new information and/or their judgement or decisions are required. The intended purpose of such automation is two pronged: a) to raise the efficiency and usability of the NFP analysis during MDE by eliminating error-prone manual model manipulations, and b) to enhance the quality of the software products by verifying the NFPs throughout the development process, from its early phases.***The short-term objectives of the proposed research are as follows: a) Development of an ecosystem of modeling artifacts which can support synchronized co-evolution of the software and analysis models; b) Inter-model traceability between two models related by a given transformation; c) Metaheuristic approaches for multi-NFP optimization; d) Semi(automated) software process tasks related to NFP analysis.*****

该方案是关于将性能、可靠性、可用性、容错性、可扩展性、安全性、可维护性、成本等多个非功能属性(NFP)的形式化分析模型无缝集成到模型驱动工程(MDE)过程中，以从早期阶段指导设计选择，并确保在建系统满足其非功能需求。这种分析模型(也称为质量模型)基于现有的形式和工具(例如，排队网络、随机Petri网、随机过程代数、马尔可夫链、故障树、概率时间自动机等)。并且通过从为开发而构建的软件模型的模型转换自动导出。开发在多个NFP之间表现出良好权衡的软件系统是困难的，因为正在建设的软件及其底层平台的设计具有跨越非常大的不连续设计空间的大量自由度，不能被穷尽地探索。另一个挑战是一些NFP是相互冲突的(例如，安全性和性能)。*拟议研究计划的长期目标是通过支持将多个NFP的分析无缝集成到MDE过程中，为模型驱动的软件工程增加更多的“工程”。基于适当的现有形式的不同NFP分析模型可以通过从为开发而构建的软件模型的模型转换来自动派生，如先前研究中所探索的。软件和NFP分析模型必须共同发展。一项重要的研究工作将是调查如何使用多个NFP分析模型来找到一个良好的(最好是最优的)设计解决方案，其中所有非功能需求都得到满足。拟议研究的另一个重要方面涉及(SEMI)自动化与NFP分析相关的软件过程任务/活动，仅当设计者需要提供新信息和/或需要他们的判断或决定时才要求人工干预。这种自动化的预期目的有两个方面：a)通过消除容易出错的手动模型操作来提高MDE期间NFP分析的效率和可用性，以及b)通过在整个开发过程中从其早期阶段验证NFP来提高软件产品的质量。*所提出的研究的短期目标如下：a)开发能够支持软件和分析模型的同步协同进化的建模构件生态系统；b)通过给定的转换相关的两个模型之间的模型间可跟踪性；c)用于多NFP优化的元启发式方法；D)与NFP分析相关的半(自动化)软件过程任务。*