Executable Code Monitoring With Critical-Chain Timing Analysis

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Solution Overview

Problem

Existing methods fail to automatically detect timing anomalies in sequences of machine language instructions, which can disrupt worst-case execution time analysis in computer architectures.

Innovation Solution

An electronic monitoring device is employed to analyze sequences of software instructions, generating modeling structures to identify critical chains and detect temporal anomalies by propagating time quantization data and checking for inconsistencies between critical chains.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automated detection of timing anomalies is implemented, then measurement precision is improved, but device complexity increases

Engineering Contradiction:
Improvetiming anomaly detection accuracyVSAvoidmonitoring device structure
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The monitoring device is segmented into distinct functional modules: an acquisition module for obtaining instruction sequences, a modeling structure generation module for creating execution path models, and a temporal anomaly detection module for identifying anomalies. This segmentation allows each module to perform its specific function efficiently while maintaining overall system manageability despite the inherent complexity of automated anomaly detection.

Inventive Principle:
Principle #1Segmentation

2Productivity

If manual analysis of instruction sequences is performed, then device complexity is reduced, but productivity decreases

Engineering Contradiction:
Improveanomaly detection efficiencyVSAvoidmonitoring system structure
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The monitoring device performs self-service by automatically acquiring instruction sequences, generating execution path models, and detecting temporal anomalies without requiring manual intervention. The system autonomously processes the analysis workflow, significantly improving productivity compared to manual methods while the modular architecture keeps the complexity manageable through clear separation of automated functions.

Inventive Principle:
Principle #25Self-service

3Measurement precision

If comprehensive modeling of execution paths is performed, then measurement precision is improved, but loss of time increases

Engineering Contradiction:
Improveexecution time measurement accuracyVSAvoidanalysis computation time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary action by generating a comprehensive modeling structure of execution paths before conducting anomaly detection. This pre-modeling phase creates a detailed representation of all possible execution paths and their temporal characteristics, enabling accurate measurement precision during the subsequent anomaly detection phase without repeating the exhaustive analysis work.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentEP4064087B1Electronic device and method for monitoring an executable code capable of being run on a computer platform, and computer program implementing such a method
Publication Date: 2025.09.17 COMMISSARIAT A LENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
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AI summary

This method of monitoring an executable code suitable for execution on a computer platform, the executable code comprising a sequence of software instructions, is implemented by an electronic device and includes: - acquisition (100) of the sequence of software instructions; - generation (110), from the sequence of instructions, of a first modeling structure of an execution path of the sequence, the first structure comprising a plurality of first groups of data, each associated with a respective instruction and comprising identifiers of a previous instruction and a following instruction;- calculation (120) of a second modeling structure of a sequence operation, the second structure comprising a plurality of second groups of data, each associated with a respective instruction and comprising an indicator of possible membership in a critical chain, and where applicable, an identifier of an initial instruction of said critical chain; the second structure being created by traversing the first groups; a critical chain corresponding to instructions of the same software function and being calculated by solving a subgraph problem with degree constraints, each critical chain corresponding to a subgraph, the number of instructions included in each critical chain being less than a predefined number, and an optimization parameter being the number of relations between the instructions of the respective critical chain, said number of relations corresponding to a number of arcs in the subgraph;and - search (140) for temporal anomaly(ies) from critical chain(s) determined via the second structure.;