Towards Human-Bot Collaborative Software Architecting with ChatGPT
- URL: http://arxiv.org/abs/2302.14600v1
- Date: Sun, 26 Feb 2023 16:32:16 GMT
- Title: Towards Human-Bot Collaborative Software Architecting with ChatGPT
- Authors: Aakash Ahmad, Muhammad Waseem, Peng Liang, Mahdi Fehmideh, Mst Shamima
Aktar, Tommi Mikkonen
- Abstract summary: Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support.
ChatGPT is a disruptive technology not primarily introduced for software engineering.
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.
- Score: 7.50312929275194
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins.
The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
arXiv Detail & Related papers (2024-10-28T13:03:09Z) - Future of Artificial Intelligence in Agile Software Development [0.0]
AI can assist software development managers, software testers, and other team members by leveraging LLMs, GenAI models, and AI agents.
AI has the potential to increase efficiency and reduce the risks encountered by the project management team.
arXiv Detail & Related papers (2024-08-01T16:49:50Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Architecture Knowledge Representation and Communication Industry Survey [0.0]
We aim to understand the current practice in architecture knowledge, and to explore where sustainability can be applied to address sustainability in software architecture in the future.
We used a survey, which utilized a questionnaire containing 34 questions and collected responses from 45 architects working at a prominent bank in the Netherlands.
arXiv Detail & Related papers (2023-09-20T18:17:16Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - Towards a Reference Software Architecture for Human-AI Teaming in Smart
Manufacturing [0.0]
We developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning.
The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain.
arXiv Detail & Related papers (2022-01-13T10:43:49Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - A Reference Software Architecture for Social Robots [64.86618385090416]
We propose a series of principles that social robots may benefit from.
These principles lay also the foundations for the design of a reference software architecture for Social Robots.
arXiv Detail & Related papers (2020-07-09T17:03:21Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.