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

arXiv: https://arxiv.org/abs/2412.06603 · HTML: https://mlsanity.com/paper/2412.06603/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2024.

## Built on by (most cited first, 17 of 17)

- [Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study](https://mlsanity.com/paper/2506.08311/) (2025)
- [Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding](https://mlsanity.com/paper/2503.16508/) (2025)
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- [Does In-IDE Calibration of Large Language Models work at Scale?](https://mlsanity.com/paper/2510.22614/) (2025)
- [Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study](https://mlsanity.com/paper/2601.16700/) (2026)
- [AI Phenomenology for Understanding Human-AI Experiences Across Eras](https://mlsanity.com/paper/2603.09020/) (2026)
- [Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph](https://mlsanity.com/paper/2604.18883/) (2026)
- [AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness](https://mlsanity.com/paper/2605.28680/) (2026)
- [Human agency in initial human-AI proof formalization workflows](https://mlsanity.com/paper/2606.04273/) (2026)

## Abstract (arXiv)

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.