AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors

Mohammad Shameem; Mahmood Niazi; Mohammad Nadeem; Ankur Kumar

doi:10.18293/SEKE2024-063

AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors

Mohammad Shameem
, Mahmood Niazi
, Mohammad Nadeem
, Ankur Kumar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Artificial intelligence (AI) is revolutionizing software development by enabling high-quality products at lower costs and faster delivery. While existing research highlights ChatGPT’s role in various phases of software development, little attention has been given to its use in end-to-end project delivery. This study explores the challenges and success factors for using AI tools in complete project workflows through interviews with software practitioners. Key challenges identified include knowledge gaps in LLMs, programming languages, and automated testing, while success factors include cost efficiency, early prototyping, and continuous improvement. These insights offer a framework for implementing AI-assisted software engineering in end-to-end projects.

Original language	English
Title of host publication	Proceedings - SEKE 2024
Subtitle of host publication	36th International Conference on Software Engineering and Knowledge Engineering
Publisher	Knowledge Systems Institute Graduate School
Pages	69-71
Number of pages	3
ISBN (Electronic)	1891706594
DOIs	https://doi.org/10.18293/SEKE2024-063
State	Published - 2024
Event	36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024 - Hybrid, San Francisco, United States Duration: 26 Oct 2024 → 4 Nov 2024

Publication series

Name	Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
ISSN (Print)	2325-9000
ISSN (Electronic)	2325-9086

Conference

Conference	36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024
Country/Territory	United States
City	Hybrid, San Francisco
Period	26/10/24 → 4/11/24

Bibliographical note

Publisher Copyright:
© 2024 Knowledge Systems Institute Graduate School. All rights reserved.

Keywords

Distributed agile process
Global software development
Systematic literature review

ASJC Scopus subject areas

Software

Access to Document

10.18293/SEKE2024-063

Cite this

Shameem, M., Niazi, M., Nadeem, M., & Kumar, A. (2024). AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors. In Proceedings - SEKE 2024: 36th International Conference on Software Engineering and Knowledge Engineering (pp. 69-71). (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE). Knowledge Systems Institute Graduate School. https://doi.org/10.18293/SEKE2024-063

Shameem, Mohammad ; Niazi, Mahmood ; Nadeem, Mohammad et al. / AI-enabled Software Engineer : A Taxonomy of Challenges and Success Factors. Proceedings - SEKE 2024: 36th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2024. pp. 69-71 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE).

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title = "AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors",

abstract = "Artificial intelligence (AI) is revolutionizing software development by enabling high-quality products at lower costs and faster delivery. While existing research highlights ChatGPT{\textquoteright}s role in various phases of software development, little attention has been given to its use in end-to-end project delivery. This study explores the challenges and success factors for using AI tools in complete project workflows through interviews with software practitioners. Key challenges identified include knowledge gaps in LLMs, programming languages, and automated testing, while success factors include cost efficiency, early prototyping, and continuous improvement. These insights offer a framework for implementing AI-assisted software engineering in end-to-end projects.",

keywords = "Distributed agile process, Global software development, Systematic literature review",

author = "Mohammad Shameem and Mahmood Niazi and Mohammad Nadeem and Ankur Kumar",

note = "Publisher Copyright: {\textcopyright} 2024 Knowledge Systems Institute Graduate School. All rights reserved.; 36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024 ; Conference date: 26-10-2024 Through 04-11-2024",

year = "2024",

doi = "10.18293/SEKE2024-063",

language = "English",

series = "Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE",

publisher = "Knowledge Systems Institute Graduate School",

pages = "69--71",

booktitle = "Proceedings - SEKE 2024",

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Shameem, M , Niazi, M, Nadeem, M & Kumar, A 2024, AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors. in Proceedings - SEKE 2024: 36th International Conference on Software Engineering and Knowledge Engineering. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, Knowledge Systems Institute Graduate School, pp. 69-71, 36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024, Hybrid, San Francisco, United States, 26/10/24. https://doi.org/10.18293/SEKE2024-063

AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors. / Shameem, Mohammad ; Niazi, Mahmood; Nadeem, Mohammad et al.
Proceedings - SEKE 2024: 36th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2024. p. 69-71 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Kumar, Ankur

PY - 2024

Y1 - 2024

N2 - Artificial intelligence (AI) is revolutionizing software development by enabling high-quality products at lower costs and faster delivery. While existing research highlights ChatGPT’s role in various phases of software development, little attention has been given to its use in end-to-end project delivery. This study explores the challenges and success factors for using AI tools in complete project workflows through interviews with software practitioners. Key challenges identified include knowledge gaps in LLMs, programming languages, and automated testing, while success factors include cost efficiency, early prototyping, and continuous improvement. These insights offer a framework for implementing AI-assisted software engineering in end-to-end projects.

AB - Artificial intelligence (AI) is revolutionizing software development by enabling high-quality products at lower costs and faster delivery. While existing research highlights ChatGPT’s role in various phases of software development, little attention has been given to its use in end-to-end project delivery. This study explores the challenges and success factors for using AI tools in complete project workflows through interviews with software practitioners. Key challenges identified include knowledge gaps in LLMs, programming languages, and automated testing, while success factors include cost efficiency, early prototyping, and continuous improvement. These insights offer a framework for implementing AI-assisted software engineering in end-to-end projects.

KW - Distributed agile process

KW - Global software development

KW - Systematic literature review

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U2 - 10.18293/SEKE2024-063

DO - 10.18293/SEKE2024-063

M3 - Conference contribution

AN - SCOPUS:85218637529

T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE

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EP - 71

BT - Proceedings - SEKE 2024

PB - Knowledge Systems Institute Graduate School

Y2 - 26 October 2024 through 4 November 2024

ER -

Shameem M , Niazi M, Nadeem M, Kumar A. AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors. In Proceedings - SEKE 2024: 36th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School. 2024. p. 69-71. (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE). doi: 10.18293/SEKE2024-063

AI-enabled Software Engineer: A Taxonomy of Challenges and Success Factors

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this