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

开源开发的广泛实践正在以显著的方式改变着IT行业。如今，开源是公司考虑的一种战略，作为其产品适销性的一部分。在科学和工程领域，开源已经有了既定的记录，如今让每个人都能获得源代码与提供支持科学主张的数据一样重要，因为科学和工程越来越依赖软件来证实主张。不幸的是，未记录的源代码就像原始的、未记录的数据一样难以理解；在不能理解的情况下提供它是没有多大好处的。尤其是开放源码项目，因为它们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以外的构件，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预算微薄而没有余地进行文档编制的软件。该项目寻求从软件构件中自动恢复高级知识，以便在没有文档的情况下使软件组件易于理解。从软件构件中恢复高级知识一直是软件工程研究的目标。到目前为止，这些成就是有限的。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。在追求这一目标的过程中，该项目展示了与开源项目相关的重要知识和工具。首先，它揭示了源代码构件之间与体系结构恢复过程相关的哪种关系以及哪种关系。其次，它将生成一个为软件组件识别量身定做的无监督学习算法目录。这将公开提供给其他人使用和学习。第三，它将产生来自不同领域的项目的软件体系结构基准。第四，它将制作一个目录，描述最好地恢复其体系结构的文物和学习技术。最后，它将产生(I)几种组件识别算法以及(Ii)结构、行为和领域特征提取的可重用实现。这个项目将所有这些知识和工具结合到一个支持软件架构自动恢复的插件中。

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.