SHF: Small: Automatic Software Architecture Recovery: A Machine Learning Approach

开源开发的广泛实践正在以重要的方式改变IT行业。如今，开源是一种策略，公司将其视为产品市场的一部分。在科学与工程领域，开放源代码已经有了良好的记录，如今让每个人都能获得源代码与让支持科学声明的数据可用同样重要，因为科学与工程越来越依赖于软件来证实声明。不幸的是，未记录的源代码和未记录的原始数据一样难以理解；有了它却不能理解它是没有多大好处的。特别是开源项目，因为缺乏文档而臭名昭著，因为开发人员通常没有资源来生成代码之外的工件，所以“代码就是文档”。这是一个普遍存在的问题，对科学的影响最大，因为它越来越依赖于在很少的预算下生产的软件，而没有为文档工作留有余地。该项目寻求从软件工件中自动恢复高级知识，以便在缺乏文档的情况下使软件组件易于理解。从软件工件中恢复高级知识一直是软件工程研究的一个长期追求的目标。迄今为止的成就是有限的。这里采用的方法是使用机器学习技术。这种方法可能最终开始为这个难以捉摸的问题产生可用的解决方案。为了实现这个目标，这个项目揭示了与开源项目相关的重要知识和工具。首先，它揭示了与架构恢复过程相关的源代码工件之间的哪些关系以及哪种关系的知识。其次，它将产生一个为软件组件识别量身定制的无监督学习算法目录。这将是公开的，供其他人使用和研究。第三，它将产生来自不同领域的项目的软件架构的基准。第四，它将产生一个目录，描述工件和最好地恢复其架构的学习技术。最后，它将产生(i)几个组件识别算法的可重用实现；（ii）结构、行为和领域特征提取。该项目将所有这些知识和工具结合在一个支持软件架构自动恢复的Eclipse插件中。

The widespread practice of open source development is changing the IT industry in significant ways. Open source, these days, is a strategy that companies consider as part of their product's marketability. In Science and Engineering, open source has an established track record, and having the source code available to everyone these days is as important as having the data supporting scientific claims available, since Science and Engineering rely more and more on software for substantiating claims. Unfortunately, undocumented source code is as difficult to understand as raw, undocumented data; having it available without being able to understand it is not of much benefit. Open source projects, in particular, are notorious for their lack of documentation, since the developers often don't have the resources to produce artifacts beyond the code, so "the code is the documentation." This is a pervasive problem that impacts Science the most, as it increasingly relies on software that is produced under slim budgets without margin for documentation efforts.This project seeks to automatically recover high-level knowledge from software artifacts in order to make software components understandable in the absence of documentation. Recovering high-level knowledge from software artifacts has been a long-sought goal of software engineering research. The achievements so far have been limited. The approach taken here is to use machine learning techniques. This approach may finally start to produce usable solutions to this elusive problem. In pursuing the goal, this project unveils important knowledge and tools related to open source projects. First, it unveils knowledge about which and what kind of relations among source code artifacts correlate with the architecture recovery process. Second, it will produce a catalog of unsupervised learning algorithms tailored for software component identification. This will be publicly available for others to use and study. Third, it will produce a benchmark of software architectures of projects from various domains. Fourth, it will produce a catalog describing the artifacts and the learning technique which best recovered their architecture. Finally, it will produce reusable implementations of (i) several component identification algorithms; and (ii) structural, behavioral, and domain feature extraction. This project combines all this knowledge and tools in a plugin for Eclipse that supports automatic recovery of software architecture.