Robot Framework Extensions

Practical extensions for advanced Robot Framework workflows.

This repository focuses on two real-world pain points:

• Dynamic keyword exposure from Python code.
• Correct logging for threaded Python execution in Robot artifacts.

It also documents a direct benchmark where the human implementation is compared against multiple AI-generated implementations for the same threading/logging task.

What Is Included

keyword_injector/

Runtime keyword injection into Robot namespace without explicit suite imports.

• Exposes Python functions and methods as executable Robot keywords.
• Keeps compatibility with BuiltIn and normal Run Keyword usage.
• Improves visibility and behavior in log.html.

See: keyword_injector/README.md

logging_threads/

Thread-aware logging flow for Robot Framework.

• Captures logs generated in worker threads.
• Replays them in the main Robot logging context after thread completion.
• Preserves ordering and readability in log.html.

See: logging_threads/README.md

Benchmark: Human Solution vs AI Solutions

This benchmark is not a generic showcase. It is a direct head-to-head comparison of:

• My solution (logging_threads.py),
• versus AI-generated solutions (Gemini 3.1 Pro, Sonnet 4.6, Opus 4.6, Opus 4.7, GPT 5.4).

All solutions are evaluated against the same task and strict acceptance criteria, especially:

• Zero-Interference (no required changes in user thread code),
• Reversibility (full cleanup after thread execution),
• structure fidelity in output.xml (FOR/IF/TRY preserved),
• no Robot Framework source edits.

Benchmark materials are located at:

• logging_threads/ai_benchmark/README.md
• logging_threads/ai_benchmark/task.txt

The benchmark includes:

• requirement-by-requirement scoring,
• technical ranking,
• architectural trade-offs and risk notes,
• final assessment of why the human implementation is the winning balance.

Quick Start

From this directory:

python -m robot logging_threads/example_suite.robot
python -m robot keyword_injector/example_suite.robot

Then inspect generated logs to verify behavior.

Why This Repository

The goal is to bridge Python flexibility with Robot Framework execution and reporting constraints, especially for:

• Dynamic and class-based library patterns.
• Concurrent execution with traceable logs.
• Production-safe behavior with minimal API interference.

Additionally, the repository serves as evidence-backed documentation of engineering decisions, where the final solution is justified through measurable comparison with AI alternatives.