# Towards Human-Bot Collaborative Software Architecting with ChatGPT

arXiv: https://arxiv.org/abs/2302.14600 · HTML: https://mlsanity.com/paper/2302.14600/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 26 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [An Analysis of the Automatic Bug Fixing Performance of ChatGPT](https://mlsanity.com/paper/2301.08653/) (2023)
- [ChatGPT and Software Testing Education: Promises & Perils](https://mlsanity.com/paper/2302.03287/) (2023)
- [How to Design a Program Repair Bot? Insights from the Repairnator Project](https://mlsanity.com/paper/1811.09852/) (2018)

## Built on by (most cited first, 30 of 31)

- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Chip-Chat: Challenges and Opportunities in Conversational Hardware Design](https://mlsanity.com/paper/2305.13243/) (2023)
- [ChatGPT for Programming Numerical Methods](https://mlsanity.com/paper/2303.12093/) (2023)
- [Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice](https://mlsanity.com/paper/2404.14901/) (2024)
- [Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis](https://mlsanity.com/paper/2402.01386/) (2024)
- [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023)
- [A Survey on Human-AI Collaboration with Large Foundation Models](https://mlsanity.com/paper/2403.04931/) (2024)
- [Automated User Story Generation with Test Case Specification Using Large Language Model](https://mlsanity.com/paper/2404.01558/) (2024)
- [Coding by Design: GPT-4 empowers Agile Model Driven Development](https://mlsanity.com/paper/2310.04304/) (2023)
- [Experimenting with Multi-Agent Software Development: Towards a Unified Platform](https://mlsanity.com/paper/2406.05381/) (2024)
- [Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review](https://mlsanity.com/paper/2409.11864/) (2024)
- [The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie](https://mlsanity.com/paper/2309.02029/) (2023)
- [The Battle of LLMs: A Comparative Study in Conversational QA Tasks](https://mlsanity.com/paper/2405.18344/) (2024)
- [Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs](https://mlsanity.com/paper/2409.00571/) (2024)
- [ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about](https://mlsanity.com/paper/2304.03325/) (2023)
- [On the Utility of Domain Modeling Assistance with Large Language Models](https://mlsanity.com/paper/2410.12577/) (2024)
- [AI for Agile development: a Meta-Analysis](https://mlsanity.com/paper/2305.08093/) (2023)
- [Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasets](https://mlsanity.com/paper/2406.17651/) (2024)
- [LLM as a code generator in Agile Model Driven Development](https://mlsanity.com/paper/2410.18489/) (2024)
- [Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models](https://mlsanity.com/paper/2506.00128/) (2025)
- [MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2507.21382/) (2025)
- [Automated Feedback on Student-Generated UML and ER Diagrams Using Large Language Models](https://mlsanity.com/paper/2507.23470/) (2025)
- [SIExVulTS: Sensitive Information Exposure Vulnerability Detection System using Transformer Models and Static Analysis](https://mlsanity.com/paper/2508.19472/) (2025)
- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)
- [DevBots can co-design APIs](https://mlsanity.com/paper/2312.05733/) (2023)
- [Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory](https://mlsanity.com/paper/2606.01385/) (2026)

## Abstract (arXiv)

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) inherits a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software (e.g., IoTs, blockchain, quantum systems). Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT for architectural analysis, synthesis, and evaluation of a services-driven software application. Preliminary results indicate that ChatGPT can mimic an architect's role to support and often lead ACSE, however; it requires human oversight and decision support for collaborative architecting. Future research focuses on harnessing empirical evidence about architects' productivity and exploring socio-technical aspects of architecting with ChatGPT to tackle emerging and futuristic challenges of ACSE.