SHF: Small: Collaborative Research: Fuzzing Cyber-Physical System Development Tool Chains with Deep Learning (DeepFuzz-CPS)

开发现代技术产品，如汽车、飞机或复杂的医疗设备，包括设计传感器（测量物理产品和环境状态）和执行器（如控制产品的小型电动机）之间复杂的相互作用。为了设计这种相互作用，工程师们依靠复杂的设计软件工具。这个项目将解决这些工程师面临的两个问题。(1)首先，很少有关于设计工具或最终设计的系统知识可以指导工程师。例如，人们对基本的设计属性（如各种设计尺寸度量）与设计质量属性（如设计复杂性和可理解性）之间的关系知之甚少。因此，该项目将收集和分析大量公开可用的设计，以建立这样的知识。(2)其次，由于设计工具很复杂，它们可能包含软件缺陷。由于从设计中生成的产品控制软件通常部署在安全关键环境中，因此这些缺陷可能反过来无声地将缺陷引入广泛部署的安全关键系统中。这类系统中的漏洞通常会导致代价高昂的产品召回，并可能产生严重后果。因此，该项目将开发自动查找此类设计工具中的软件错误的技术。本项目由以下三个主要部分组成。(1)首先，该项目将建立公开可用的网络物理系统模型和相关工件的最大策划语料库。分析该语料库的初步结果既证实了早期基于较少模型的发现，也反驳了早期的发现，表明大型语料库对未来研究的实用性。(2)其次，为了避免网络物理系统工具链缺乏完整形式规范的老问题，本项目将设计一种新的方案，通过深度学习从项目的模型语料库中推断网络物理系统语言有效性规则。对深度学习器进行采样将为研究人员现有的差异网络物理系统工具链测试基础设施生成额外的模型。(3)第三，本项目将通过基于等价模输入的第一个系统网络物理系统模型突变方案来补充深度学习者的训练集。最初的实验已经在商业网络物理系统工具链中发现了几个漏洞，这些漏洞已经得到了工具链供应商的证实。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Developing a modern technical product such as a car, plane, or a complex medical device includes designing the complex interplay between sensors (which measure physical product and environment state) and actuators (such as small electric motors that control the product). To design this interplay, engineers rely on complex design software tools. This project will address two problems these engineers face. (1) First, little systematic knowledge of the design tools or the resulting designs is available to guide engineers. For example, little is known about how basic design properties (such as various design size measures) relate to design quality attributes (such as design complexity and comprehensibility). This project will thus collect and analyze a large number of publicly available designs to build such knowledge. (2) Second, since the design tools are complex they can contain software bugs. These bugs may in turn silently introduce bugs into widely-deployed safety-critical systems, since product control software generated from designs is often deployed in safety-critical environments. Bugs in such systems often lead to costly product recalls and may have serious consequences. This project will thus develop techniques for automatically finding software bugs in such design tools. This project consists of the following three major components. (1) First, this project will build the largest curated corpus of publicly available cyber-physical system models and related artifacts. Preliminary results analyzing this corpus both confirms and contradicts earlier findings that are based on significantly fewer models, suggesting the utility of a large corpus for future research. (2) Second, to side-step the age-old problem of missing complete formal specifications of cyber-physical system tool chains, this project instead will design a novel scheme to infer the cyber-physical system language validity rules via deep learning from the project's model corpus. Sampling the deep learner will enable generating additional models for the researchers' existing differential cyber-physical system tool chain testing infrastructure. (3) Third, this project will supplement the deep learner's training set via the first systematic cyber-physical system-model mutation scheme based on equivalence modulo inputs. Initial experiments have found several bugs in a commercial cyber-physical system tool chain that have been confirmed by the vendor of the tool chain.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.