docs(python): add GCI91 feasibility report and green metrics - REWORK#482
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Pull request overview
Adds Python-specific feasibility/metrics documentation for rule GCI91 (“filter before sort”) and updates the rules support matrix to reflect that the rule is now considered implementable for Python.
Changes:
- Added a new Python AsciiDoc report for GCI91 with benchmark protocol and green software metrics.
- Updated
RULES.mdto move Python’s GCI91 status from “to analyze” to “to implement”.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| src/main/rules/GCI91/python/GCI91.asciidoc | Adds a Python-focused feasibility/benchmark report for GCI91. |
| RULES.md | Updates the Python applicability status for GCI91 in the support matrix. |
Comments suppressed due to low confidence (1)
src/main/rules/GCI91/python/GCI91.asciidoc:63
- The Python rule document doesn’t include any non-compliant/compliant code examples, which is inconsistent with other Python rule docs (e.g., GCI96/python/GCI96.asciidoc includes explicit examples) and makes the rule harder to apply/implement. Consider adding a short “Code Examples” section showing the sort-then-filter anti-pattern and the filter-then-sort compliant pattern.
== 6. Recommendation
Based on clear empirical evidence tracking hardware power consumption, implementing GCI91 delivers substantial infrastructure relief and carbon reduction without adding syntax complexity. We recommend shifting the rule state to applicable for Python.
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| = GCI91 Rule Feasibility and Optimization Report: Use Filter Before Sort | ||
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| == 1. Objective and Context | ||
| The purpose of this report is to evaluate the applicability and green software metrics of rule GCI91 (Use filter feature before sort elements) for the Python language. This analysis aims to justify changing its status from pending evaluation to active within the standard matrix. |
| - RAM Energy Draw Optimization: Divided by 4.50 | ||
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| == 5. Algorithmic and Structural Analysis | ||
| The massive variance in resource draw is mathematically sound. Python uses Timsort, an adaptive sorting algorithm derived from Merge Sort and Insertion Sort, operating with a worst-case time complexity of O(n log n). |
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This PR was created from original #459
to rework it and take into account review feedbacks.