CAREER: Scalable and Trustworthy Automatic Program Repair

修复软件错误是一个困难且耗时的过程，占一个典型程序生命周期成本的90%。由于缺陷的数量超过了修复它们的可用资源，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、全自动的方法上，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。该研究将广泛适用，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伸缩的、可信赖的技术来自动修复程序错误。程序变体使用类似的生物过程（如突变）进行进化，直到发现其中一个既保留了所需的功能又避免了缺陷。成千上万的标准软件测试用例、程序不变量、挖掘的规范和“模糊”输入被用来表示错误、编码程序需求，并构建可信的修复。经验评估包括一个自动强化场景：随着时间的推移，程序和针对它们的攻击是共同进化的，模拟了部分安全军备竞赛。重要的潜在成果包括：可扩展且值得信赖的自动程序修复方法和免费提供的工具；形式程序分析的研究进展在外联和教育方面作出重大努力；以及结果的传播。

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.