SHF: Small: Synthesizing Human-Readable Documentation

SHF：小型：综合人类可读的文档

• 批准号: 1116289
• 负责人: Westley Weimer
• 金额: $ 47.1万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116289&HistoricalAwards=false
• 关键词: SHF; Small; Synthesizing; Human; Readable

Developing and maintaining software is a key challenge in computer science,with failures costing up to one half of one percent of the US GDP eachyear. Most code is retained and evolved, rather than created from scratch,and professional software developers spend over three-fourths of their timetrying to understand existing code. Understandability and documentationhave become key components of software quality, yet they remain poorlyunderstood by both researchers and practitioners. In a future where thesoftware engineering focus shifts from implementation to design andcomposition concerns, program understandability will become even moreimportant. This research develops tools and techniques for mechanicallygenerating documentation to help make programs easier to understand.The research follows the insight that modern analysis techniques can formrich descriptive models of programs that are both precise and succinct.Human-readable documentation can then be synthesized from such models.The approach applies to large programs across multiple application domains.The research focuses on documenting how code should be used correctly, acritical aspect in an era of components-of-the-shelf development, as wellas documenting how code has changed and evolved over time, a keypart of software maintenance. The research leverages program analysistechniques, machine learning, and textual synthesis, with resultsdisseminated through academic publication; the education, training andmentoring of students; as well as freely-available, open-source tools.

开发和维护软件是计算机科学的一个关键挑战，每年的故障成本高达美国GDP的0.5%。大多数代码都是保留和发展的，而不是从头开始创建的，专业软件开发人员花费超过四分之三的时间试图理解现有代码。可理解性和文档化已经成为软件质量的关键组成部分，但研究人员和实践者对它们的理解仍然很差。在软件工程的重点从实现转向设计和组合关注的未来，程序的可理解性将变得更加重要。本研究开发了用于机械生成文档的工具和技术，以帮助使程序更容易理解。本研究遵循现代分析技术可以形成精确和简洁的程序的丰富描述模型的见解。这种方法适用于跨多个应用程序域的大型程序。研究重点是记录代码应该如何使用正确地说，这是现成组件开发时代的关键方面，同时也记录了代码如何随着时间的推移而变化和发展，这是软件维护的关键部分。该研究利用程序分析技术，机器学习和文本合成，通过学术出版物传播结果;学生的教育，培训和指导;以及免费提供的开源工具。