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Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Towards Human-Bot Collaborative Software Architecting with ChatGPT
AU - Ahmad, Aakash
AU - Waseem, Muhammad
AU - Liang, Peng
AU - Fahmideh, Mahdi
AU - Aktar, Mst Shamima
AU - Mikkonen, Tommi
PY - 2023/6/14
Y1 - 2023/6/14
N2 - 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) suffers from 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. 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 to architect a service-based software. Future research focuses on harnessing empirical evidence about architects' productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.
AB - 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) suffers from 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. 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 to architect a service-based software. Future research focuses on harnessing empirical evidence about architects' productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.
KW - ChatGPT
KW - DevBots
KW - Large Language Models
KW - Software Architecture
U2 - 10.1145/3593434.3593468
DO - 10.1145/3593434.3593468
M3 - Conference contribution/Paper
T3 - ACM International Conference Proceeding Series
SP - 279
EP - 285
BT - Proceedings of EASE 2023 - Evaluation and Assessment in Software Engineering
PB - ACM
CY - New York
ER -