# SWEPR (Stanford Software Engineering Productivity Research)

#entity

## Summary

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.

## Current Understanding

- Site: https://softwareengineeringproductivity.stanford.edu/ — offers an "AI Practices Benchmark" and "AI Impact" research to participating companies.
- 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).
- 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?").
- 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).

## Evidence

- All claims and links — [[2026-07-30-stanford-swepr-widening-gap]].

## Related Pages

- Sources: [[2026-07-30-stanford-swepr-widening-gap]]
- 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)
- Queries: [[2026-07-30-stanford-widening-gap-source]]
- Entities: [[allie-miller]] (her prediction, their measurement)

## Contradictions / Uncertainty

- Data is proprietary and opt-in — no outside replication possible; participating companies may skew toward the measurement-friendly. Status: tentative.
- The widening-gap analysis itself is not peer-reviewed as of 2026-07-30. Status: tentative.

## Next Questions

- Track whether the DiD analysis lands in a peer-reviewed venue.
- What do they say *causes* the gap (tooling vs hygiene vs practices)? The talks assert "quality of usage" without decomposing it.