mlsanityData to 14 Oct 2026

Intuition to Evidence: Measuring AI's True Impact on Developer Productivity

arXiv 2509.19708 ↗First posted 24 Sep 2025cs.SE cs.AI cs.LGcited by 5

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Abstract (arXiv)

We present a comprehensive real-world evaluation of AI-assisted software development tools deployed at enterprise scale. Over one year, 300 engineers across multiple teams integrated an in-house AI platform (DeputyDev) that combines code generation and automated review capabilities into their daily workflows. Through rigorous cohort analysis, our study demonstrates statistically significant productivity improvements, including an overall 31.8% reduction in PR review cycle time. Developer adoption was strong, with 85% satisfaction for code review features and 93% expressing a desire to continue using the platform. Adoption patterns showed systematic scaling from 4% engagement in month 1 to 83% peak usage by month 6, stabilizing at 60% active engagement. Top adopters achieved a 61% increase in code volume pushed to production, contributing to approximately 30 to 40% of code shipped to production through this tool, accounting for an overall 28% increase in code shipment volume. Unlike controlled benchmark evaluations, our longitudinal analysis provides empirical evidence from production environments, revealing both the transformative potential and practical deployment challenges of integrating AI into enterprise software development workflows.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 52 references that are in the map. First posted on arXiv 24 Sep 2025.

Builds on

17 of 52 references.

References · 17

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2021Program Synthesis with Large Language Modelscited by 3,637
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  4. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
  5. 2023LongCoder: A Long-Range Pre-trained Language Model for Code Completioncited by 112
  6. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 101
  7. 2024Long Code Arena: a Set of Benchmarks for Long-Context Code Modelscited by 50
  8. 2024How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmarkcited by 43
  9. 2023RepoFusion: Training Code Models to Understand Your Repositorycited by 40
  10. 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 29
  11. 2024AI-Assisted Assessment of Coding Practices in Modern Code Reviewcited by 16
  12. 2024LiCoEval: Evaluating LLMs on License Compliance in Code Generationcited by 14
5 more
  1. 2024DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generationcited by 12
  2. 2024Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generationcited by 11
  3. 2024A Transformer-Based Approach for Smart Invocation of Automatic Code Completioncited by 5
  4. 2025Towards Practical Defect-Focused Automated Code Reviewcited by 3
  5. 2025DeputyDev -- AI Powered Developer Assistant: Breaking the Code Review Logjam through Contextual AI to Boost Developer Productivitycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.