CRII: SHF: Towards a Cognizant Virtual Software Modeling Assistant using Model Clones

软件在社会中越来越无处不在。相应地，软件质量，包括安全性和可靠性，比以往任何时候都更加重要。软件故障会对个人和国家经济造成重大问题。安全也是一个最重要的社会问题。这个研究项目通过改进软件建模来帮助解决质量问题，软件建模是软件开发中的一个关键阶段，工程师在这个阶段指定软件做什么以及它是如何工作的。它迈出了第一步，允许工程师以其他人的经验和最佳实践的形式利用数据中的知识，将已建立的模型合并到他们的软件中。这个项目解决了以下几个基本问题：1)正确地收集这些数据，2)获得关于这些数据的有用见解，以及3)最好地将这些见解和建议呈现给工程师。通过提供这种帮助和信息，工程师可以做出更明智的决定，从而为整个社会提供更高质量的软件。除了帮助工程师，软件建模是STEM课程的一个重要方面，包括计算机科学、软件工程和其他工程学科。教师可以利用该奖项衍生的方法和工具作为教学工具，帮助学生批判性地思考设计决策。这将产生更优秀的计算机科学家和工程师，他们更熟悉和精通正式的软件建模。模型驱动工程（MDE）是一种用于构建大规模安全质量软件系统的正式方法。该奖项将通过实现一个可认知的虚拟软件建模助手来提高软件设计和MDE的质量。该项目使用模型克隆检测在开发过程中分析模型，从相同的领域和/或最佳实践中找到类似的模型，并将这些相似的模型作为训练数据进行推理，以便向用户建议模型的添加和修改。这种帮助将产生与基于过去使用统计数据的类似源代码方法所提供的好处相似的好处。这涉及到一个探索性的研究，以开发一种新的方法和原型虚拟软件建模助手使用已建立的模型克隆检测器。在本研究的第一阶段，研究者将构建一个原型，该原型具有分析工程师正在构建/扩展的不完整模型的能力，并根据与来自同一领域和/或最佳实践的模型的相似性提出插入的完整模型。在第二阶段，研究人员将创建一个助手，该助手根据分析类似模型产生细粒度建议，并根据这些操作在这些类似模型形成的知识库中的流行程度，向工程师提供他们可能想要执行的操作选项。本研究的见解和数据将为构建更高级的建模助手、对该方法进行用户研究和教育评估提供必要的基础，并为建模的认知奠定基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Software is growing increasingly omnipresent in society. Correspondingly, software quality, which includes both security and reliability, is more important than ever. Software failures can cause significant problems to both individuals and national economies. Security is also a paramount societal concern. This research project helps address quality by improving software modeling, a critical stage in software development where engineers specify what the software does and how it works. It takes the first step in allowing engineers to leverage knowledge from data in the form of others' experiences and best practices to incorporate established models for use in their software. This project tackles the fundamental issues of how to 1) gather this data properly, 2) derive useful insights about that data, and 3) best present these insights and suggestions to engineers. By providing this assistance and information, engineers can make better-informed decisions, thus yielding higher quality software for all society. In addition to helping engineers, software modeling is an important aspect of the STEM curriculum, including computer science, software engineering, and other engineering disciplines. Instructors can utilize the approaches and tools derived from this award as a teaching tool by helping students think critically about design decisions. This will yield better computer scientists and engineers who are more comfortable and versed in formal software modeling.Model-driven engineering (MDE) is an established formal methodology for building large-scale secure quality software systems. This award will improve that quality by realizing a cognizant virtual software-modeling assistant to improve software design and MDE. This project uses model-clone detection to analyze models during development, finds similar models from the same domain and/or best practices, and treats those similar models as training data to reason about in order to suggest model additions and modifications to users. Such assistance will yield similar benefits to those afforded by analogous source-code approaches based on past usage statistics. This involves an exploratory investigation to develop a new approach and prototype virtual software-modeling assistant using an established model clone detector. In the first phase of this research, the investigator will build a prototype with the capability to analyze incomplete models being constructed/extended by engineers to suggest completed models for insertion based on similarity to those from the same domain and/or best practices. In the second phase, the investigator will create an assistant that produces granular suggestions based on analyzing similar models, and presents options to engineers of operations they may want to do next based on those operations' prevalence in the knowledge base formed by those similar models. This research's insights and data will provide the foundation necessary to build more advanced modeling assistants, conduct user studies and educational assessments of the approach, and help lay a foundation for the cognification of modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.