Intuition to Evidence: Measuring AI's True Impact on Developer Productivity
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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
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2021Program Synthesis with Large Language Modelscited by 3,637
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
- 2023LongCoder: A Long-Range Pre-trained Language Model for Code Completioncited by 112
- 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 101
- 2024Long Code Arena: a Set of Benchmarks for Long-Context Code Modelscited by 50
- 2024How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmarkcited by 43
- 2023RepoFusion: Training Code Models to Understand Your Repositorycited by 40
- 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 29
- 2024AI-Assisted Assessment of Coding Practices in Modern Code Reviewcited by 16
- 2024LiCoEval: Evaluating LLMs on License Compliance in Code Generationcited by 14
5 more
- 2024DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generationcited by 12
- 2024Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generationcited by 11
- 2024A Transformer-Based Approach for Smart Invocation of Automatic Code Completioncited by 5
- 2025Towards Practical Defect-Focused Automated Code Reviewcited by 3
- 2025DeputyDev -- AI Powered Developer Assistant: Breaking the Code Review Logjam through Contextual AI to Boost Developer Productivitycited by 3
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Developmentcited by 11
- 2026CodeScout: Contextual Problem Statement Enhancement for Software Agentscited by 4
- 2026Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLIcited by 1
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requestscited by 0
- 2026Engineering Signals of Human-AI Collaboration in the Agentic Coding Era: A Longitudinal Analysis of 33,228 Pull Requests from vLLM and SGLang with Implications for Biomedical AI Agents and Bioinformatics Pipeline Developmencited by 0
- 2026Engineering Signals of Human-AI Collaboration in the Agentic Coding Era: A Longitudinal Analysis of 33,228 Pull Requests from vLLM and SGLang with Implications for Biomedical AI Agents and Bioinformatics Pipeline Developmen14 Aug 2026
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests23 Jul 2026
- 2026Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI1 Jul 2026
- 2026CodeScout: Contextual Problem Statement Enhancement for Software Agents5 Mar 2026
- 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development20 Jan 2026