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Add two new sources with summaries, new concepts (developer-as-agent-manager, review-is-the-new-bottleneck), new entities (SWEPR, Nikolai Sheiko), and a query on the Stanford source; update related concept pages, overview, index, and log.
36 lines
2.2 KiB
Markdown
36 lines
2.2 KiB
Markdown
# SWEPR (Stanford Software Engineering Productivity Research)
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#entity
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## Summary
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Research group at Stanford University measuring software-engineering productivity from private Git data — 600+ companies, ~100k–120k engineers since 2022 — using an ML model that replicates a panel of expert code reviewers. Public face: researcher **Yegor Denisov-Blanch**. Source of the corpus's only quantitative outside study of AI's productivity impact.
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## Current Understanding
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- Site: https://softwareengineeringproductivity.stanford.edu/ — offers an "AI Practices Benchmark" and "AI Impact" research to participating companies.
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- Known for three results: the **widening-gap DiD analysis** (46 vs 46 teams, gap 4.8% → 19%, 4×, Apr 2023–Jul 2025), the **~15–20% net average gain** figure (after rework; 30–40% gross), and the earlier **"ghost engineers"** finding (~9.5% of engineers show virtually no verifiable output).
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- Methodology is peer-reviewed (arXiv 2409.15152, 2502.20747); the headline AI-impact analyses are talk/deck-published (Sept 2025 AI Conference deck "Will AI Replace Software Engineers?").
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- Their measurement philosophy — functionality delivered, not commits or LOC — aligns with the corpus's own suspicion of volume metrics ([[make-more-cheap-code]]: generation volume is the *cheap* part).
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## Evidence
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- All claims and links — [[2026-07-30-stanford-swepr-widening-gap]].
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## Related Pages
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- Sources: [[2026-07-30-stanford-swepr-widening-gap]]
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- Concepts: [[levels-of-ai-usage]] (team-level twin of the mastery gap), [[context-as-scarce-resource]] (codebase-size finding), [[make-more-cheap-code]] (review-time shift)
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- Queries: [[2026-07-30-stanford-widening-gap-source]]
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- Entities: [[allie-miller]] (her prediction, their measurement)
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## Contradictions / Uncertainty
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- Data is proprietary and opt-in — no outside replication possible; participating companies may skew toward the measurement-friendly. Status: tentative.
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- The widening-gap analysis itself is not peer-reviewed as of 2026-07-30. Status: tentative.
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## Next Questions
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- Track whether the DiD analysis lands in a peer-reviewed venue.
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- What do they say *causes* the gap (tooling vs hygiene vs practices)? The talks assert "quality of usage" without decomposing it.
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