Automotive Lighting State Machine Code Reasoning Benchmark

#code understanding #state transition reasoning #control logic analysis #automotive lighting control #vehicle ECU software validation #automotive embedded systems

5000
0.5GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-19

AI Analysis & Value Prop

This benchmark focuses on mode management and state transitions in automotive lighting ECUs, pairing relevant source code with annotations of state machine behavior. It covers state names, transition events and conditions, and lighting control or other ECU outputs associated with each state. Use it to assess whether models can reconstruct state behavior and key transition logic from code, and to evaluate code understanding and reasoning in automotive lighting electronics.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
source_code	string	Original source code related to lighting ECU mode management and state transitions.
state_names	array	A list of state names identified in the source code.
transitions	array	State transitions reconstructed from the source code, including their triggering events and transition conditions.
state_outputs	array	Lighting control outputs or other ECU output behavior summarized for each state.
behavior_summary	string	A summary of the state machine's overall behavior, key transition paths, and output logic.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the benchmark contain?: It focuses on mode management and state machine logic in automotive lighting ECUs. Records include related source code, identified state names, transitions with their triggering events and conditions, and lighting control or other ECU outputs organized by state.

Which capabilities can this data evaluate?: It can assess whether a model can identify states from source code, reconstruct transition paths and conditions, and infer output behavior for different states. Typical uses include code understanding, code reasoning, and specialized evaluation in automotive lighting electronics.

How is the data organized, and can it support automated evaluation?: Records pair source code with structured state machine information, supporting evaluation tasks such as code question answering, transition analysis, and output logic inference. Refer to the product page or delivery documentation for the exact field format and access method.

What are the licensing and usage terms?: The data may be used for model evaluation, research, and development subject to the applicable license. Redistribution of source code, commercial use, and other uses must follow the actual license agreement; check the product page for the relevant permissions and delivery terms before use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Automotive Lighting State Machine Code Reasoning Benchmark},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/d5555655897f00956d0faf893f95d092},
  urldate={2026-10-12},
  keywords={automotive lighting state machine, ECU code reasoning, automotive source code benchmark, state transition analysis, lighting control logic, code understanding benchmark, automotive electronics dataset, domain model evaluation},
  version={1.0}
}

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