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How much does AI impact development speed? An enterprise-based randomized controlled trial

arXiv 2410.12944 ↗First posted 16 Oct 2024cs.SE cs.HCcited by 19

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Abstract (arXiv)

How much does AI assistance impact developer productivity? To date, the software engineering literature has provided a range of answers, targeting a diversity of outcomes: from perceived productivity to speed on task and developer throughput. Our randomized controlled trial with 96 full-time Google software engineers contributes to this literature by sharing an estimate of the impact of three AI features on the time developers spent on a complex, enterprise-grade task. We found that AI significantly shortened the time developers spent on task. Our best estimate of the size of this effect, controlling for factors known to influence developer time on task, stands at about 21%, although our confidence interval is large. We also found an interesting effect whereby developers who spend more hours on code-related activities per day were faster with AI. Product and future research considerations are discussed. In particular, we invite further research that explores the impact of AI at the ecosystem level and across multiple suites of AI-enhanced tools, since we cannot assume that the effect size obtained in our lab study will necessarily apply more broadly, or that the effect of AI found using internal Google tooling in the summer of 2024 will translate across tools and over time.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2024.

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Citing papers per year: 2024: 1; 2025: 6; 2026: 12.

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  1. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 93
  2. 2025CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Codingcited by 11
  3. 2025RESCUE: Retrieval Augmented Secure Code Generationcited by 7
  4. 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agentscited by 6
  5. 2026The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Developmentcited by 3
  6. 2026Life After Benchmark Saturation: A Case Study of CORE-Benchcited by 3
  7. 2024Follow the money: a startup-based measure of AI exposure across occupations, industries and regionscited by 2
  8. 2026Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Developmentcited by 2
  9. 2025Understanding and supporting how developers prompt for LLM-powered code editing in practicecited by 1
  10. 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
  11. 2025AI-Assisted Fixes to Code Review Comments at Scalecited by 0
  12. 2025Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation Systemcited by 0
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  2. 2026HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detectioncited by 0
  3. 2026Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Codecited by 0
  4. 2026Characterizing the Quality Profile of AI-Generated C++ in Productioncited by 0
  5. 2026SDAD: Spec-Driven Agentic Development for the AI-Native SDLCcited by 0
  6. 2026A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoptioncited by 0
  7. 2026Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflowscited by 0