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: 1563726
• 负责人: Kathryn Stolee
• 金额: $ 38.77万
• 依托单位: Iowa 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563726&HistoricalAwards=false
• 关键词: 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都严重依赖于软件的正确工作。然而，软件错误是常见的，通常会导致安全漏洞，每年给我们的经济造成数十亿美元的损失。尽管众所周知软件错误的成本很高，但软件行业仍在努力克服这一挑战，因为报告新错误的速度比修复它们的速度要快。最近的研究表明，自动程序修复技术的潜力，以解决这一挑战。 在这项研究中，我们开发了新的技术来修复软件错误和自动实现新功能。挑战在于在不破坏其他功能的情况下修复代码，并努力修复日益复杂的代码。该方法利用了开源代码的高可用性，这些代码已经实现了新软件项目所需的许多功能。该方法是在开源项目中搜索相关代码，使用自动软件修复和生成技术使代码适应新的上下文，然后验证更改后的软件。该方法的一个关键组成部分是语义代码搜索，它查询大型代码数据库以查找满足行为规范的代码片段。该项目开发了新的技术，（1）将大型可搜索的代码体编码为行为配置文件，（2）将错误和功能定位到代码块，模块和组件，（3）提取这些块，模块和组件的所需行为配置文件，（4）使用提取的配置文件搜索数据库中的潜在补丁，（5）调整潜在补丁以适应代码上下文，以及（6）验证潜在补丁。该项目专注于生成高质量的代码，验证注入的代码不会破坏现有的功能。 更广泛的影响主要来自通过重用和适应现有代码从根本上提高软件生产力的目标。