CAREER: Quality Matters: Dynamic, Static and Proactive Analyses for Automated Program Repair

职业：质量很重要：自动程序修复的动态、静态和主动分析

• 批准号: 1750116
• 负责人: Claire Le Goues
• 金额: $ 52.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750116&HistoricalAwards=false
• 关键词: CAREER; Quality; Matters; Dynamic; Static

Society is increasingly reliant on software that affects human safety, privacy, and other important concerns. Software engineers have many tools at their disposal to identify errors and write correct code. However, programmer productivity and software quality are serious problems due to the number of open bugs, the negative impact of these bugs, and limited time available to developers to fix them. A recently emerging area of research develops techniques for automatic program repair. This project will advance the state of software testing and repair by addressing some of the challenges that must be overcome if automatic program repair is to become a common tester's tool. The outcomes of this research will reduce the cost of software defects, improve the quality of software, and improve productivity of programmers.To attack this problem, the project will develop repair techniques that synthesize new code based on Hoare logic and provide better patch guidance for developers through automatic reduction of false negatives. The research will extend separation logic-based bug-finding analyses to construct verified fixes. This involves dynamic strategies that search for and then compose diverse solutions to a given bug repair problem into measurably general patches, also static techniques that construct patches for previously undiscovered bugs that are difficult to find via testing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

社会越来越依赖影响人体安全，隐私和其他重要问题的软件。软件工程师有许多可以使用的工具来识别错误并编写正确的代码。但是，由于开放错误的数量，这些错误的负面影响以及开发人员可用于修复它们的时间有限，因此程序员的生产率和软件质量是严重的问题。最近新兴的研究领域开发了用于自动程序维修的技术。 该项目将通过解决自动程序维修成为普通测试师的工具必须克服的一些挑战来推动软件测试和维修的状态。这项研究的结果将降低软件缺陷的成本，提高软件的质量并提高程序员的生产率。为了攻击此问题，该项目将开发基于Hoare逻辑的新代码，并通过自动减少假否定性来综合新代码，并为开发人员提供更好的补丁指南。该研究将扩展基于分离逻辑的错误调查分析以构建经过验证的修复程序。这涉及动态策略，这些策略搜索并构成了给定的错误维修问题的多样化解决方案，这些解决方案也可以衡量的一般贴片，还静态技术，这些技术为以前未发现的虫子构造了斑块，这些虫子难以通过测试难以通过测试找到。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识优点和广泛的crietia进行评估，并被认为是值得通过评估的支持。

项目成果

Common Statement Kind Changes to Inform Automatic Program Repair

• DOI: 10.1145/3196398.3196472
• 发表时间: 2018-05
• 期刊: 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR)
• 作者: Mauricio Soto;Claire Le Goues

Automated Program Repair

• DOI: 10.1145/3318162
• 发表时间: 2019-12-01
• 期刊: COMMUNICATIONS OF THE ACM
• 影响因子: 22.7
• 作者: Le Goues, Claire;Pradel, Michael;Roychoudhury, Abhik
• 通讯作者: Roychoudhury, Abhik

ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems

ROSDiscover：静态检测机器人系统中的运行时架构错误配置

• DOI: 10.1109/icsa53651.2022.00019
• 发表时间: 2022
• 期刊: 2022 IEEE 19th International Conference on Software Architecture (ICSA
• 作者: Timperley, Christopher S.;Durschmid, Tobias;Schmerl, Bradley;Garlan, David;Le Goues, Claire
• 通讯作者: Le Goues, Claire

An Empirical Study of OSS-Fuzz Bugs

• DOI: 10.1109/msr52588.2021.00026
• 发表时间: 2021-03
• 期刊: 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)
• 作者: Zhen Yu Ding;Claire Le Goues

Semantic Crash Bucketing

• DOI: 10.1145/3238147.3238200
• 发表时间: 2018-09
• 期刊: 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Rijnard van Tonder;John Kotheimer;Claire Le Goues