Do AI coding agents improve software development productivity?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   The Impact of AI on Developer Productivity: Evidence from GitHub Copilot

   arXiv (Cornell University) (Peng et al.)Published Feb 13, 2023Checked Oct 10, 2026

   “Generative AI tools hold promise to increase human productivity. This paper presents results from a controlled experiment with GitHub Copilot, an AI pair programmer. Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible. The treatment group, with access to the AI pair programmer, completed the task 55.8% faster than the control group. Observed heterogenous effects show promise for AI pair programmers to help people transition into software development careers.”

2. 2

   The Productivity Effects of Generative AI: Evidence from a Field Experiment with GitHub Copilot

   Research paper (Cui et al.)Published Mar 27, 2024Checked Oct 10, 2026

   “We are providing a preview of a project that analyzes two field experiments with 1,974 software developers at Microsoft and Accenture to evaluate the productivity impact of Generative AI. As part of our study, a random subset of developers was given access to GitHub Copilot, an AI-based coding assistant that intelligently suggests ‘completions’ for code. Our preliminary results provide suggestive evidence that these developers became more productive, completing 12.92% to 21.83% more pull requests per week at Microsoft and 7.51% to 8.69% at Accenture (depending on specification). Due to low compliance in the Microsoft experiment and internal organizational changes at Accenture, our estimates are not very precise and only reach statistical significance if we weight more heavily periods in which Copilot uptake differs across control and treatment.”

3. 3

   Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise

   arXiv (Cornell University) (Weisz et al.)Published Dec 9, 2024Checked Oct 10, 2026

   “AI assistants are being created to help software engineers conduct a variety of coding-related tasks, such as writing, documenting, and testing code. We describe the use of the watsonx Code Assistant (WCA), an LLM-powered coding assistant deployed internally within IBM. Through surveys of two user cohorts (N=669) and unmoderated usability testing (N=15), we examined developers' experiences with WCA and its impact on their productivity. We learned about their motivations for using (or not using) WCA, we examined their expectations of its speed and quality, and we identified new considerations regarding ownership of and responsibility for generated code. Our case study characterizes the impact of an LLM-powered assistant on developers' perceptions of productivity and it shows that although such tools do often provide net productivity increases, these benefits may not always be experienced by all users.”

4. 4

   Fragmented Markers, Mixed Results: A Systematic Review of AI Coding Assistants and Developer Productivity

   IEEE/ACM International Conference on Automated Software Engineering (ASE) (Wittig et al.)Published Oct 7, 2026Checked Oct 10, 2026

   “AI coding assistants are increasingly embedded in software engineering practice, yet the literature still offers no cumulative, easily interpretable picture of their effects on software developer productivity. The central problem is not a lack of studies, but a lack of comparability: prior work operationalizes productivity through heterogeneous markers, settings, and reporting styles, making cross-study conclusions difficult. To address this, we conduct a staged systematic literature review. In the first stage, we synthesize studies that explicitly examine developer productivity. In the second, we extend the scope to productivity-related developer outcomes using SPACE as a theory-informed lens, capturing human-centered evidence that explicit productivity studies often omit. Across both stages, we analyze how productivity is operationalized, which confounding factors are discussed, and how reported effects are distributed.”

5. 5

   AI coding agents generate more code, but not more software

   Ars TechnicaPublished Oct 9, 2026Checked Oct 10, 2026

   “Study finds coding efficiency gains get "absorbed" by human review "bottleneck."”

6. 6

   Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants

   arXiv (Cornell University) (Chen et al.)Published Feb 3, 2026Checked Oct 10, 2026

   “In the age of AI coding assistants, it has become even more important for both academia and industry to understand how to measure their impact on developer productivity, and to reconsider whether earlier measures and frameworks still apply. This study analyzes the validity of different approaches to evaluating the productivity impacts of AI coding assistants by leveraging mixed-method research. At BNY Mellon, we conduct a survey with 2989 developer responses and 11 in-depth interviews. Our findings demonstrate that a multifaceted approach is needed to measure AI productivity impacts: survey results expose conflicting perspectives on AI tool usefulness, while interviews elicit six distinct factors that capture both short-term and long-term dimensions of productivity. In contrast to prior work, our factors highlight the importance of long-term metrics like technical expertise and ownership of work. We hope this work encourages future research to incorporate a broader range of human-centered factors, and supports industry in adopting more holistic approaches to evaluating developer productivity.”