SHF: Small: Contract Inference for Polyglot Programming

SHF：小：多语言编程的契约推理

• 批准号: 1318489
• 负责人: Benjamin Liblit
• 金额: $ 38.43万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318489&HistoricalAwards=false
• 关键词: SHF; Small; Contract; Inference; Polyglot

Software engineers use various "programming languages" to write the software that runs on our computers, smart phones, cars, and other devices. Modern software is a complex mix of pieces written in many different languages. But cross-language communication is difficult and error-prone, for humans and machines alike. The PI's research will ease the burden of developers working across multiple programming languages by automatically generating the "glue" that sticks different fragments together, which will let developers work in more modern, safe, high-level languages while still benefiting from the large existing investment in older, unsafe, low-level code. The result will be faster, cheaper development by productive programmers, leading to more trustworthy software to benefit society as a whole.Specifically, the PI will use a variety of static program analyses to address three recurring challenges of multi-language software development: disallowed values, resource management, and error reporting. Disallowed values, such as unexpected NULL pointers, are a form of API misuse that can lead to failures or undefined behaviors. By identifying these and trapping them in high-level code before they reach low-level libraries, the PI's research will allow safer execution and improved failure diagnostics. Resource management requires uncovering the subtle, sometimes quirky rules by which low-level libraries track memory, file descriptors, and other resources. Static analyses to recover resource management models will let these be managed automatically by the improved mechanisms (e.g., garbage collectors) available in high-level code. Lastly, error reporting analysis will identify ad hoc error propagation mechanisms (such as returned error codes) used in low-level libraries and map these to the structured high-level mechanisms (such as exceptions) that high-level application developers expect. The net effect of these will be improved library bindings that let high-level programmers do their jobs more efficiently, introducing fewer bugs, creating tomorrow's great software for all of us to rely upon.

软件工程师使用各种“编程语言”来编写在我们的计算机、智能手机、汽车和其他设备上运行的软件。现代软件是用许多不同的语言编写的复杂的组合。但是，对于人类和机器来说，跨语言交流都是困难和容易出错的。PI的研究将通过自动生成将不同片段粘合在一起的“粘合剂”来减轻开发人员在多种编程语言中工作的负担，这将使开发人员在使用更现代、更安全、更高级的语言工作的同时，仍然受益于现有对较旧、不安全、低级别代码的大量投资。具体来说，PI将使用各种静态程序分析来解决多语言软件开发中反复出现的三个挑战：不允许的值、资源管理和错误报告。不允许的值，如意外的空指针，是API误用的一种形式，可能导致失败或未定义的行为。通过识别它们并在它们到达低级库之前将它们捕获到高级代码中，PI的研究将允许更安全的执行和改进的故障诊断。资源管理需要揭示低级库跟踪内存、文件描述符和其他资源的微妙规则，有时甚至是奇怪的规则。恢复资源管理模型的静态分析将让高级代码中可用的改进机制(例如，垃圾收集器)自动管理这些模型。最后，错误报告分析将识别低级库中使用的特殊错误传播机制(如返回的错误代码)，并将这些机制映射到高级应用程序开发人员期望的结构化高级机制(如异常)。这些改进的最终结果将是改进的库绑定，让高级程序员更高效地完成他们的工作，引入更少的错误，创造出我们所有人都可以依赖的未来伟大的软件。