SHF: Small: Collaborative Research: Towards Automated Model Synthesis of Library and System Functions for Program-Environment Co-Analysis

SHF：小型：协作研究：面向程序-环境协同分析的库和系统功能的自动模型综合

• 批准号: 1319705
• 负责人: Feng Qin
• 金额: $ 15万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319705&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Towards

The rapid advance of program analysis greatly benefits many applications, including security vulnerability detection, software fault localization, performance optimization, to name a few. However, handling library functions and system calls (also referred to as environmental functions) presents a pervasive and critical challenge in program analysis. Even though these environmental functions are not written by developers, they are an intrinsic part of program semantics and consequently it would be ideal for a program analysis to co-analyze the program and its execution environment. Despite its importance, achieving program-environment co-analysis in practice is challenging. First, the difficulty to acquire the source code of some environmental functions precludes source code based analysis. Moreover, even if source code is available, the code base is often prohibitively large and complex, making analysis difficult.In this project, the goal is to develop a highly automated technique that can construct models for environmental functions from their binary implementations and a set of initial inputs. The models are essentially programs that provide the same functionality of the functions being modeled, yet substantially simplified. Such programs can be included as part of the application, enabling program-environment co-analysis. The proposed technique will lead to a highly automated solution that will largely offload the onus of manually crafting models from program analysis developers' shoulders. Moreover, it will make program environment co-analysis feasible and more precise, enabling detection of security vulnerability and software defects that are otherwise undetectable. Additionally, the PIs expect the proposed research to foster learnings in both program analysis and operating systems, as well as providing many opportunities to incorporate findings to relevant courses in computer science.

程序分析的快速进步极大地使许多应用程序受益，包括安全漏洞检测，软件故障本地化，性能优化等等。但是，处理库功能和系统调用（也称为环境功能）在程序分析中提出了普遍且至关重要的挑战。即使这些环境功能不是由开发人员撰写的，它们是程序语义的内在组成部分，因此，对于程序分析，可以共同分析程序及其执行环境是理想的选择。尽管它很重要，但在实践中实现计划环境共同分析是具有挑战性的。首先，获取某些环境功能的源代码的困难排除了基于源代码的分析。此外，即使有源代码可用，代码库通常通常是大而复杂的，使分析变得困难。在此项目中，目标是开发一种高度自动化的技术，该技术可以从其二进制实现和一组初始输入中为环境功能构建模型。这些模型本质上是提供与正在建模的功能相同功能的程序，但基本简化了。这些程序可以作为申请的一部分包括在内，从而实现了环境的共同分析。所提出的技术将导致高度自动化的解决方案，从而在很大程度上将手动制定模型的责任从程序分析开发人员的肩膀上卸下。此外，它将使程序环境共同分析可行，更精确，从而可以检测到否则无法检测到的安全漏洞和软件缺陷。此外，PIS期望拟议的研究能够在程序分析和操作系统中培养学习，并提供许多机会，将发现结果纳入计算机科学的相关课程。