DevTest Daniel Cáceres

[dcaceres-tech]

This PR is my implementation of the Elevator Prediction System. The data generation for ML is fully functional, and you can test the APIs and run a demo data generation with the following command:

python main.py
python -m pytest test_elevator.py -v
python demo.py

See Elevator_Prediction_System_Design.pdf for a detailed explanation of my design approach.

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 30%

Reasoning: The pull request contains a fully developed and well-documented software project for an "Elevator Prediction System," involving REST APIs, a simulation system, an ML-ready data logging backend, a testing suite, database schema, and a demo script. While the explanatory text (like in the README.md or structured docstring comments) is clear and concise—which could raise questions about AI assistance—several aspects of the implementation strongly suggest human authorship. The code includes thoughtful architecture, edge case handling in unit tests, natural language phrasing with informal and context-appropriate comments, and consistent style across components. Even the README, while polished, reflects a practical engineering point of view rather than a purely synthetic or generic tone.

Key Indicators:

• Contextual Awareness: Tests are tightly integrated with the operational logic (e.g., ensuring elevator is in a resting state before expecting logging behavior).
• Complexity and Cohesion: The entire end-to-end pipeline (simulation → logging → ML training readiness) appears too context-dependent and deliberate to be fully AI-generated.
• Realistic Naming and Modularity: Variable and function names are semantically clear and purpose-driven. AI often introduces generic or overly abstract naming.
• Informal Commentary: Inline comments and print statements throughout the demo script mimic natural human pedagogical explanation styles.
• Logical Flow in Design Decisions: The “Golden Event” model is described and implemented in a way that shows both predictive insight and semantic abstraction, traits typically seen in experienced developers.

While AI tools could have augmented certain parts of this submission (e.g., markdown formatting or generating boilerplate code), the overall cohesion, iterative logic, and test coverage suggest substantial human authorship.

Thus, there is low confidence this pull request is AI-generated.

✅ No strong indicators of AI generation detected