SHF: Medium: Collaborative Research: Semi and Fully Automated Program Repair and Synthesis via Semantic Code Search

SHF：媒介：协作研究：通过语义代码搜索进行半自动化和全自动程序修复和合成

• 批准号: 1645136
• 负责人: Kathryn Stolee
• 金额: $ 38.77万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645136&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Semi; SHF; Medium; Collaborative; Research; Semi

Many aspects of our economy rely heavily on software working correctly. However, software errors are common, routinely cause security breaches, and cost our economy billions of dollars annually. Despite the well-known high costs of software errors, the software industry struggles to overcome this challenge, as new errors are reported faster than they can be fixed. Recent research has demonstrated the potential of automated program repair techniques to address this challenge. In this research, we develop new techniques to fix software errors and implement new features automatically. The challenge is to fix code while not breaking other functionality, and to work toward repairing code of increasing complexity.The approach takes advantage of the high availability of open-source code that already implements many functions required for a new software project. The approach is to search for relevant code in open-source projects, adapt that code to its new context using automated software repair and generation techniques, and then validate the changed software. A key component of the approach is semantic code search, which queries large databases of code to find code snippets that satisfy a behavioral specification. The project develops novel techniques that (1) encode large, searchable bodies of code as behavioral profiles, (2) localize bugs and features to code blocks, modules, and components, (3) extract the desired behavioral profiles of those blocks, modules, and components, (4) use the extracted profiles to search the database for potential patches, (5) adapt the potential patches to fit into the code context, and (6) validate the potential patches. The project focuses on producing high-quality code, verifying that the injected code does not break existing functionality. The broader impacts come mainly from goal of radically improving software productivity through reuse and adaptation of existing code.

我们经济的许多方面在很大程度上依赖于正确工作的软件。但是，软件错误很常见，通常会导致安全漏洞，并每年损失我们经济的数十亿美元。尽管软件错误的高昂成本众所周知，但软件行业仍在努力克服这一挑战，因为据报道，新错误的速度快于固定的速度快。最近的研究表明，自动化计划维修技术的潜力以应对这一挑战。 在这项研究中，我们开发了用于修复软件错误并自动实施新功能的新技术。面临的挑战是修复代码的同时不破坏其他功能，并致力于修复增加复杂性的代码。该方法利用了已经实现新软件项目所需的许多功能的开源代码的高可用性。这种方法是在开源项目中搜索相关代码，使用自动化软件维修和发电技术将该代码调整到其新上下文中，然后验证已更改的软件。该方法的关键组成部分是语义代码搜索，该搜索查询大量代码数据库以查找满足行为规范的代码段。该项目开发了（1）编码大型的，可搜索的代码作为行为概况的大型，（2）将错误和特征定位到代码块，模块和组件中，（3）提取这些块，模块和组件的所需行为概况，以搜索潜在的pertections，以搜索潜在的pertentate和portecters potters and（4）适合势能的pertentate and（4）（4）潜在的补丁。该项目的重点是生产高质量的代码，验证注入的代码不会破坏现有功能。 更广泛的影响主要来自通过重复使用和适应现有代码从根本上提高软件生产率的目标。