CAREER: Scalable and Trustworthy Automatic Program Repair

职业：可扩展且值得信赖的自动程序修复

基本信息

批准号：
0954024
负责人：
Westley Weimer
金额：
$ 40万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2017-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954024&HistoricalAwards=false
关键词：
CAREER Scalable Trustworthy Automatic Program

项目摘要

Fixing software bugs is a difficult and time-consuming process, accounting for up to 90% of the lifetime cost of a typical program. Because the number of defects outstrips the resources available for repairing them, most software is shipped with both known and unknown bugs. This research builds upon a novel, fully-automated method for repairing bugs in existing software, producing trustworthy repairs for real-world programs. The research will be broadly applicable, targeting large legacy applications with many testcases.The technical focus of the work is a scalable and trustworthy technique to automatically repair program bugs. Program variants are evolved, using analogues of biological processes such as mutation, until one is found that both retains required functionality and avoids the defect. Thousands of standard software testcases, program invariants, mined specifications, and "fuzz" inputs are used to represent bugs, encode program requirements, and build trusted repairs. Empirical evaluations include an automated hardening scenario: programs and attacks against them are coevolved over time, simulating parts of the security arms race. Significant potential outcomes include: a scalable and trustworthy automated program repair methodology and freely available tools; advances in formal program analyses; significant efforts in outreach and education; and dissemination of the results.

修复软件错误是一个困难且耗时的过程，占典型程序生命周期成本的 90%。由于缺陷的数量超出了可用于修复它们的资源，因此大多数软件都带有已知和未知的错误。这项研究建立在一种新颖的全自动方法的基础上，用于修复现有软件中的错误，为现实世界的程序提供值得信赖的修复。该研究将具有广泛的适用性，针对具有许多测试用例的大型遗留应用程序。这项工作的技术重点是一种可扩展且值得信赖的自动修复程序错误的技术。使用生物过程的类似物（例如突变）来进化程序变体，直到发现一种程序变体既保留了所需的功能又避免了缺陷。数千个标准软件测试用例、程序不变量、挖掘的规范和“模糊”输入用于表示错误、编码程序需求和构建可信修复。实证评估包括自动强化场景：程序和针对它们的攻击随着时间的推移而共同演化，模拟部分安全军备竞赛。重要的潜在成果包括：可扩展且值得信赖的自动化程序修复方法和免费提供的工具；正式计划分析的进展；在外展和教育方面作出重大努力；和结果的传播。