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: 1446932
• 负责人: Kathryn Stolee
• 金额: $ 7.3万
• 依托单位: Iowa State 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446932&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正确工作的软件。但是，软件中的错误会导致安全漏洞，并使我们每年经济损失数十亿美元。尽管这些错误的高成本是众所周知的，但软件行业努力纠正这种情况，因为该软件的固有复杂性使错误如此普遍，以至于通常比开发人员可以更快地报告新错误。 该项目的目的是开发一种技术来修复错误的技术，大大降低了修复错误的成本，提高软件的质量，并减少对经济和社会的负面影响。因为已经编写了很多软件，因此已经编写了许多次级，数据结构和算法实施，这已经存在于开放式软件的一部分。因此，对于许多软件错误，在其他开源软件中已经存在子例程，数据结构和算法实现，这些软件可以实现正确的行为，并可以替换为buggy系统以修复错误。该项目验证构建这种错误修复技术所需的两个关键属性。首先，该项目试图验证以下假设：正确的代码候选物实际上存在于开源软件代码库中。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代码候选物，并且可以使用自动技术桥接正确和不正确版本之间的差距。总的来说，这个探索性项目旨在通过对开源软件的语义搜索来确定自动错误修复的可行性。这项工作的更广泛的影响是提高软件质量的技术的进步，进而减少软件错误的负面经济和社会影响。 这项赠款是关于未经测试但可能具有变革性的研究思想的探索性工作。