SHF: Small: Inferring Specifications for Blackbox Code

SHF：小：推断黑盒代码规范

基本信息

批准号：
1910769
负责人：
Osbert Bastani
金额：
$ 50万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910769&HistoricalAwards=false
关键词：
SHF Small Inferring Specifications Blackbox

项目摘要

Writing error-free computer software is notoriously difficult. Yet, software errors (bugs) can have catastrophic consequences, ranging from loss of user data and security vulnerabilities to loss of property and even loss of life. Thus, designing tools that help software developers identify and fix bugs is of critical importance. The goal of this project is to contribute to the development of these kinds of tools. In particular, a key challenge faced by all such tools is the need for specifications that describe the properties of software libraries shared among many applications. Often, without these specifications, the library code cannot be analyzed, substantially diminishing the usefulness of bug-finding tools. This project's novelty lies in the development of algorithms that use machine learning to automatically infer these kinds of specifications. The project outcomes will substantially improve the usefulness of bug-finding tools, thereby reducing the number of bugs in software.As a part of this project, the researchers design novel and general algorithms for inferring specifications for blackbox code (i.e., code that can be executed but not instrumented or analyzed). Focusing on the blackbox setting ensures that the algorithms will work in a broad range of settings, ranging from native code to code only available over a network connection. These algorithms infer specifications by executing the code on carefully chosen inputs, observing the corresponding output, and then generalizing the observed behaviors to specifications. Furthermore, to improve performance, these algorithms use reinforcement-learning to automatically learn domain-specific search strategies, eliminating the need for end users to manually design heuristic search strategies for their problem domain.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.

编写无错误的计算机软件是出了名的困难。然而，软件错误（bug）可能会带来灾难性的后果，从用户数据丢失和安全漏洞到财产损失甚至生命损失。因此，设计帮助软件开发人员识别和修复错误的工具至关重要。该项目的目标是为此类工具的开发做出贡献。特别是，所有此类工具面临的一个关键挑战是需要描述许多应用程序之间共享的软件库的属性的规范。通常，如果没有这些规范，就无法分析库代码，从而大大降低了错误查找工具的实用性。该项目的新颖之处在于开发使用机器学习自动推断此类规格的算法。该项目的成果将大大提高错误查找工具的实用性，从而减少软件中的错误数量。作为该项目的一部分，研究人员设计了新颖且通用的算法来推断黑盒代码的规范（即可以被执行但未检测或分析）。专注于黑盒设置可确保算法在广泛的设置中工作，从本机代码到仅通过网络连接可用的代码。这些算法通过在精心选择的输入上执行代码、观察相应的输出，然后将观察到的行为概括为规范来推断规范。此外，为了提高性能，这些算法使用强化学习来自动学习特定领域的搜索策略，从而消除最终用户为其问题领域手动设计启发式搜索策略的需要。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。