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）之间的复杂相互作用。为了设计这种相互作用，工程师依赖于复杂的设计软件工具。该项目将解决这些工程师面临的两个问题。(1)首先，很少有设计工具或最终设计的系统知识可用于指导工程师。例如，很少有人知道基本的设计属性（如各种设计尺寸的措施）与设计质量属性（如设计的复杂性和可理解性）。因此，该项目将收集和分析大量公开的设计，以建立这样的知识。(2)其次，由于设计工具很复杂，它们可能包含软件错误。这些错误可能会反过来悄悄地将错误引入广泛部署的安全关键系统，因为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。此类系统中的漏洞往往导致昂贵的产品召回，并可能产生严重后果。因此，该项目将开发自动发现此类设计工具中软件错误的技术。该项目包括以下三个主要部分。(1)首先，该项目将建立最大的公开可用的网络物理系统模型和相关工件的策展语料库。分析该语料库的初步结果既证实了也反驳了早期的研究结果，这些研究结果是基于显着较少的模型，这表明大型语料库对未来研究的实用性。(2)其次，为了避开网络物理系统工具链缺少完整形式规范的古老问题，该项目将设计一种新的方案，通过从项目的模型语料库中进行深度学习来推断网络物理系统语言的有效性规则。对深度学习者进行采样将能够为研究人员现有的差分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，该项目将通过第一个基于等价模输入的系统化网络物理系统模型变异方案来补充深度学习器的训练集。最初的实验发现了商业网络物理系统工具链中的几个错误，这些错误已被工具链的供应商确认。该奖项反映了NSF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.