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Illustrative fixture

Harvest Timing Fixture: Node, Python, and azd

By FrootAI Engine Team · CC0-1.0 · Source declared: Pre-filled sample-data.json timings; no runner logs or Azure OpenAI execution receipt is attached

Not citation-grade evidence

The script replays pre-filled timings. No 50-repository corpus, run logs, environment receipt, raw timing samples, or implementation-parity evidence is attached.

Do not use this fixture to claim cost, carbon, performance, or product superiority.

Fixture implementations

3

Results by Category

Milliseconds (median, Node implementation, n=50 repos)

View data table
CategoryNode pipeline
S1 Discover320
S2 Fetch480
S3 Extract1,200
S4 Retrieve650
S5 Scaffold980
S6 Compose380
S7 Customize190

Fixture Observations

  • 1.The fixture provides seven stage values and three implementation totals.
  • 2.The script identifies Node as fastest only because the supplied Node total is the smallest fixture value.
  • 3.No measured performance comparison or feature-parity conclusion is supported by the attached evidence.

Fixture Design

The reproduction script reads pre-filled stage and total timings from sample-data.json, selects the smallest supplied median, and writes results.json. It does not clone 50 repositories, execute Node or Python Harvest pipelines, run azd, call Azure OpenAI, or collect timing samples.

Limitations

  • No pipeline implementation is executed by the reproduction script.
  • No raw timing distribution or 50-repository corpus is attached.
  • The compared workflows are not demonstrated to be feature-equivalent.
  • The fixture cannot support a performance or superiority claim.

Replay This Fixture

Replay the deterministic sample transformation. The script, sample data, and expected output are in the repository:

git clone https://github.com/frootai/frootai-core
cd frootai-core/scripts/benchmarks/pipeline-speed
bash run.sh
View script on GitHub