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docs: add Borg et al. 2025 RCT on AI code maintainability (v3.27.7)

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- Resource eval: arXiv:2507.00788 "Echoes of AI" (151 devs, 95% pros,
  2-phase blind RCT) — 30.7% faster median, ~55.9% habitual users,
  no significant downstream maintainability impact
- guide/learning-with-ai.md: citation + "On maintainability fear" note
- guide/ultimate-guide.md: nuance blockquote in §1.7 Trust Calibration
- machine-readable/reference.yaml: 4 new RCT/maintainability entries
- docs/resource-evaluations/: evaluation file with technical-writer audit

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Florian BRUNIAUX 2026-02-19 09:59:50 +01:00
parent 4c42151151
commit 895ace49f7
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machine-readable/reference.yaml
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@ -91,6 +91,11 @@ deep_dive:
learning_embracing_ai: "guide/learning-with-ai.md:518"
learning_30day_plan: "guide/learning-with-ai.md:710"
learning_red_flags: "guide/learning-with-ai.md:770"
# Productivity Research RCTs
productivity_rct_metr: "guide/learning-with-ai.md:925" # METR 2025: experienced devs 19% slower on large codebases despite perceiving 20% faster
productivity_rct_echoes: "guide/learning-with-ai.md:926" # Borg 2025: 30.7% faster (median), ~55.9% habitual users, no maintainability impact downstream
productivity_maintainability_empirical: "guide/learning-with-ai.md:926" # Empirical data on "AI code is unmaintainable" claim — blind RCT shows no significant difference
trust_calibration_maintainability_nuance: "guide/ultimate-guide.md:1092" # Nuance: defect rates ≠ maintenance burden (Borg et al. 2025)
learning_mode_template: "examples/claude-md/learning-mode.md"
learn_quiz_command: "examples/commands/learn/quiz.md"
learn_teach_command: "examples/commands/learn/teach.md"