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

SHF：小型：自动软件架构恢复：机器学习方法

• 批准号: 1218228
• 负责人: Cristina Lopes
• 金额: $ 50万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218228&HistoricalAwards=false
• 关键词: SHF; Small; Automatic; Software; Architecture

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.

开源开发的广泛实践正在以重大方式改变IT行业。如今，开源是公司将其视为其产品可销售性的一部分的策略。在科学和工程学中，开源有既定的往绩，并且在这些天，拥有每个人的源代码与拥有支持科学索赔的数据一样重要，因为科学和工程越来越多地依赖于软件来证明索赔。不幸的是，无证源代码与原始的，无证件数据一样难以理解。没有能够理解它并没有太大的好处。尤其是开源项目因缺乏文档而臭名昭著，因为开发人员通常没有资源来生产守则以外的文物，因此“代码是文档”。这是一个普遍影响科学的普遍问题，它越来越依赖于在纤薄的预算下生产的软件而没有文档工作的余地。该项目试图自动从软件文物中恢复高级知识，以使软件组件在缺乏文档的情况下可以理解。从软件工件中恢复高级知识一直是软件工程研究的长期目标。到目前为止的成就受到限制。这里采用的方法是使用机器学习技术。这种方法最终可能开始为这个难以捉摸的问题提供可用的解决方案。在追求目标时，该项目揭示了与开源项目相关的重要知识和工具。首先，它揭示了有关源代码伪像之间哪种关系以及与体系结构恢​​复过程相关的知识。其次，它将生成针对软件组件识别的无监督学习算法的目录。这将公开供其他人使用和学习。第三，它将产生来自各个领域项目的软件架构的基准。第四，它将生成一个描述工件和学习技术的目录，从而最能恢复其建筑。最后，它将产生（i）多种组件识别算法的可重复使用的实现； （ii）结构，行为和域特征提取。该项目将所有这些知识和工具结合在用于日食的插件中，以支持软件体系结构的自动恢复。