# Enhancing Software Development with Context-Aware Conversational Agents: A User Study on Developer Interactions with Chatbots

arXiv: https://arxiv.org/abs/2505.08648 · HTML: https://mlsanity.com/paper/2505.08648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 13 May 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 3 of 3)

- [Parallel Context Windows for Large Language Models](https://mlsanity.com/paper/2212.10947/) (2022)
- [A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering](https://mlsanity.com/paper/2012.02640/) (2020)
- [Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks](https://mlsanity.com/paper/1802.01096/) (2018)

## Abstract (arXiv)

Software development is a cognitively intensive process requiring multitasking, adherence to evolving workflows, and continuous learning. With the rise of large language model (LLM)-based tools, such as conversational agents (CAs), there is growing interest in supporting developers through natural language interaction. However, little is known about the specific features developers seek in these systems. We conducted a user study with 29 developers using a prototype text-based chatbot to investigate preferred functionalities. Our findings reveal strong interest in task automation, version control support, and contextual adaptability, especially the need to tailor assistance for both novice and experienced users. We highlight the importance of deep contextual understanding, historical interaction awareness, and personalized support in CA design. This study contributes to the development of context-aware chatbots that enhance productivity and satisfaction, and it outlines opportunities for future research on human-AI collaboration in software engineering.