SHF: EAGER: Collaborative Research: Demonstrating the Feasibility of Automatic Program Repair Guided by Semantic Code Search

SHF：EAGER：协作研究：展示语义代码搜索引导的自动程序修复的可行性

• 批准号: 1446966
• 负责人: Claire Le Goues
• 金额: $ 8万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446966&HistoricalAwards=false
• 关键词: SHF; EAGER; Collaborative; Research; Demonstrating

Software is an integral part of our everyday lives, and our economy relies heavily on software working correctly. However, bugs in software cause security breaches, and cost our economy billions of dollars annually. While these high costs of bugs are well known, the software industry struggles to remedy the situation because the inherent complexity of the software makes bugs so common that new bugs are typically reported faster than developers can fix them. The goal of this project is to develop a technique that fixes bugsautomatically, greatly reducing the cost of fixing the bugs, improving quality of software, and reducing the negative effects on the economy and society.Because so much software has already been written, many subroutines, data structures, and algorithm implementations already exist as part of open-source software. Therefore, for many software bugs, there already exist subroutines, data structures, and algorithm implementations in other open-source software that implement the correct behavior and can be substituted into buggy systems to fix the bugs. This project verifies two key properties necessary to build such a bug fixing technique. First, the project attempts to validate the assumption that correct code candidates actually exist in open-source software code bases. Second, the project aims to demonstrate that semantic code search techniques can effectively find these code candidates, and that the gaps between the correct and incorrect versions can be bridged using automatic techniques. Altogether, this exploratory project is intended to establish the feasibility of automated bug fixing through semantic search of open-source software. The broader impact of this work is the advancement of techniques that improve software quality, which, in turn, reduces the negative economic and societal effects of software bugs. This grant is exploratory work on an untested, but potentially transformative, research idea.

软件是我们日常生活中不可或缺的一部分，我们的经济在很大程度上依赖于软件的正常运行。然而，软件错误会导致安全漏洞，每年给我们的经济造成数十亿美元的损失。虽然错误的高昂成本是众所周知的，但软件行业一直在努力纠正这种情况，因为软件固有的复杂性使得错误如此普遍，以至于新错误的报告速度通常比开发人员修复它们的速度要快。 该项目的目标是开发一种自动修复错误的技术，大大降低修复错误的成本，提高软件质量，减少对经济和社会的负面影响。由于已经编写了如此多的软件，许多子程序、数据结构和算法实现已经作为开源软件的一部分存在。因此，对于许多软件错误，其他开源软件中已经存在实现正确行为的子例程、数据结构和算法实现，并且可以替换到有错误的系统中来修复错误。该项目验证了构建此类错误修复技术所需的两个关键属性。首先，该项目试图验证开源软件代码库中实际存在正确候选代码的假设。其次，该项目旨在证明语义代码搜索技术可以有效地找到这些候选代码，并且可以使用自动技术来弥合正确版本和错误版本之间的差距。总而言之，这个探索性项目旨在通过开源软件的语义搜索建立自动错误修复的可行性。这项工作更广泛的影响是提高软件质量的技术进步，从而减少软件错误对经济和社会的负面影响。 这笔资助是对未经测试但具有潜在变革性的研究想法的探索性工作。