Model-Driven Optimization in Software Engineering

软件工程中的模型驱动优化

• 批准号: 462887453
• 负责人: Professorin Dr. Gabriele Taentzer
• 金额: --
• 依托单位: Arbeitsgruppe Softwaretechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/462887453?language=en
• 关键词: Model; Driven; Optimization; Software; Engineering

A variety of software engineering problems can be considered as optimization problems such as software modularization, software testing, and release planning. In search-based software engineering (SBSE) meta-heuristic methods are applied to solve optimization problems in software engineering. One of the widely used approaches to iteratively explore a search space are evolutionary algorithms. The problem domains in software engineering are typically encoded with vectors or trees since evolutionary operators can be specified straightforwardly. When the quality of optimization results is not as high as expected, an explanation for this effect may be that domain-specific knowledge is not captured enough in the explorative search. Model-driven engineering (MDE) offers concepts, methods and techniques to process domain-specific models uniformly. The use of MDE in SBSE is called model-driven optimization (MDO); it has been demonstrated at well-known optimization problems in the literature. MDO is promising as domain-specific knowledge can be systematically incorporated into SBSE. To strengthen the MDO vision, this project aims to consolidate MDO, i.e., to develop a scientific basis for the results obtained so far and to obtain a deeper understanding when and how MDO shall be used to solve optimization problems in software engineering. This project vision can be broken down into the following objectives: (1) Develop a formal framework for MDO that defines a uniform approach for specifying optimization problems and evolutionary algorithms using domain-specific knowledge. The framework will be used for clarifying concepts and for reasoning about the quality of evolutionary algorithms in MDO such that developers can make informed decisions. (2) Perform an empirical evaluation of MDO to investigate its practical relevance. Two topical subject fields of SBSE have been identified for this evaluation, namely mutation testing and genetic improvement of programs. As a prerequisite for this evaluation, an integrated tool environment for MDO will be developed taking all concepts and results of the formal framework into account that are practically relevant.

各种软件工程问题都可以被视为优化问题，例如软件模块化、软件测试和发布计划。在基于搜索的软件工程（SBSE）中，元启发式方法被应用于解决软件工程中的优化问题。迭代探索搜索空间的广泛使用的方法之一是进化算法。软件工程中的问题域通常用向量或树进行编码，因为可以直接指定进化算子。当优化结果的质量不如预期时，对此效果的解释可能是在探索性搜索中没有充分捕获特定领域的知识。模型驱动工程（MDE）提供了统一处理特定领域模型的概念、方法和技术。在SBSE中使用MDE称为模型驱动优化（MDO）；它已在文献中的著名优化问题中得到证明。 MDO 前景广阔，因为特定领域的知识可以系统地融入 SBSE 中。为了强化 MDO 愿景，该项目旨在巩固 MDO，即为迄今为止所取得的成果奠定科学基础，并更深入地了解何时以及如何使用 MDO 来解决软件工程中的优化问题。该项目愿景可分为以下目标： (1) 开发 MDO 的正式框架，定义使用特定领域知识指定优化问题和进化算法的统一方法。该框架将用于澄清概念并推理 MDO 中进化算法的质量，以便开发人员能够做出明智的决策。 (2)对MDO进行实证评估，探讨其实际意义。此次评估确定了 SBSE 的两个主题领域，即突变测试和程序遗传改良。作为本次评估的先决条件，将开发一个多域作战的集成工具环境，同时考虑到正式框架中实际相关的所有概念和结果。