CPS Control Code Analysis for Explaining Operational Deviations
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Solution Overview
Problem
Cyber-physical systems (CPS) face challenges in identifying deviations from expected operation due to code vulnerabilities, which can result in unsafe behavior, and traditional analysis techniques fail to detect changes in actuation conditions and inputs, making troubleshooting difficult and time-consuming.
Innovation Solution
A data-driven model extraction and validation framework that uses the HyMN algorithm to generate a hybrid system representation of CPS behavior, compares it with an abstract syntax tree representation of expected behavior, and identifies discrepancies in modes and transition conditions to detect faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis techniques are used to detect faults in CPS, then the system structure remains simple, but the ability to detect changes in actuation conditions and inputs is insufficient
Solution Approach 1:
The patent segments the CPS control code into an abstract syntax tree (AST) representation, dividing the code into hierarchical components (functions, variables, conditions). This segmentation enables precise localization of faults by comparing specific code segments between the original and deployed systems, thereby improving fault detection capability while managing complexity through structured analysis.
Solution Approach 2:
The patent introduces an intermediary framework that includes a code parser, AST generator, and diff analysis module. This intermediary layer translates the complex task of fault detection into a series of manageable steps: parsing control code into AST, comparing AST representations, and generating fault explanations. The intermediary framework bridges the gap between simple traditional techniques and the need for precise fault detection.
2Reliability
If manual troubleshooting of CPS is performed, then the troubleshooting process can be thorough, but it becomes difficult and time-consuming
Solution Approach 1:
The patent performs preliminary action by automatically generating and storing the AST representation of the original control code before deployment. This pre-computed AST serves as a reference baseline that can be quickly compared against the deployed system's AST, eliminating the need for manual code analysis during troubleshooting. The preliminary preparation enables rapid fault detection while maintaining thoroughness through systematic comparison.
Solution Approach 2:
The patent implements feedback by automatically generating fault explanations that provide actionable insights into what changed in the deployed code. The system compares the original AST with the deployed AST, identifies discrepancies in actuation conditions and inputs, and feeds back specific fault locations and nature to the user. This automated feedback loop replaces time-consuming manual troubleshooting with rapid, informed diagnostic information.
3Measurement precision
If the CPS control code is analyzed in detail to identify faults, then measurement precision improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent replaces manual mechanical code analysis with automated computational analysis using AST parsing and diff algorithms. Instead of manually examining control code line-by-line, the system uses computer-based tools to automatically parse code into structured AST representations, compare these representations, and identify faults. This substitution of manual mechanical analysis with automated computational methods improves precision while managing complexity through algorithmic processing.
Data Source
AI summary
Complexities arising from interaction between multiple inter-connected components in an autonomous cyber physical system can potentially result in differences in intended and observed operations of a CPS. To perform fault analysis of a CPS it is imperative to explain this discrepancy in terms of the components of CPS control code. A system estimates a “mined” hybrid system representation of a CPS based on observed input/output traces and extracts a state machine representation of the CPS control code. The system compares the “mined” hybrid system representation with the extracted state machine representation simplification of the CPS code to identify discrepancies between expected and observed operation of the CPS. The system explains discrepancies in terms of call conditions as binary or unary operations on input and output variables and status of function call arguments.