mlsanityData to 7 Oct 2026

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

arXiv 2412.06603 ↗First posted 9 Dec 2024cs.HC cs.SEcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2024.

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Citing papers per year: 2025: 8; 2026: 9.

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  1. 2025Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Studycited by 16
  2. 2025Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Codingcited by 11
  3. 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Developmentcited by 10
  4. 2025Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programmingcited by 2
  5. 2026An Exploratory Study on LLM-Generated Code and Comments in Code Repositoriescited by 2
  6. 2026Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learningcited by 1
  7. 2026From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineeringcited by 1
  8. 2025Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agentscited by 0
  9. 2025Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Taskscited by 0
  10. 2025AI and Agile Software Development: A Research Roadmap from the XP2025 Workshopcited by 0
  11. 2025Reverse Browser: Vector-Image-to-Code Generatorcited by 0
  12. 2025Does In-IDE Calibration of Large Language Models work at Scale?cited by 0
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  1. 2026Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Studycited by 0
  2. 2026AI Phenomenology for Understanding Human-AI Experiences Across Erascited by 0
  3. 2026Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraphcited by 0
  4. 2026AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulnesscited by 0
  5. 2026Human agency in initial human-AI proof formalization workflowscited by 0