# From Human-to-Human to Human-to-Bot Conversations in Software Engineering

arXiv: https://arxiv.org/abs/2405.12712 · HTML: https://mlsanity.com/paper/2405.12712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 7 of 7)

- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents](https://mlsanity.com/paper/1901.06525/) (2019)
- [Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice](https://mlsanity.com/paper/2404.14901/) (2024)
- [A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering](https://mlsanity.com/paper/2012.02640/) (2020)
- [MSRBot: Using Bots to Answer Questions from Software Repositories](https://mlsanity.com/paper/1905.06991/) (2019)
- [With Great Humor Comes Great Developer Engagement](https://mlsanity.com/paper/2312.01680/) (2023)

## Built on by (most cited first, 1 of 1)

- [REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage](https://mlsanity.com/paper/2604.01527/) (2026)

## Abstract (arXiv)

Software developers use natural language to interact not only with other humans, but increasingly also with chatbots. These interactions have different properties and flow differently based on what goal the developer wants to achieve and who they interact with. In this paper, we aim to understand the dynamics of conversations that occur during modern software development after the integration of AI and chatbots, enabling a deeper recognition of the advantages and disadvantages of including chatbot interactions in addition to human conversations in collaborative work. We compile existing conversation attributes with humans and NLU-based chatbots and adapt them to the context of software development. Then, we extend the comparison to include LLM-powered chatbots based on an observational study. We present similarities and differences between human-to-human and human-to-bot conversations, also distinguishing between NLU- and LLM-based chatbots. Furthermore, we discuss how understanding the differences among the conversation styles guides the developer on how to shape their expectations from a conversation and consequently support the communication within a software team. We conclude that the recent conversation styles that we observe with LLM-chatbots can not replace conversations with humans due to certain attributes regarding social aspects despite their ability to support productivity and decrease the developers' mental load.