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Authors: Ferenc Héjja 1 ; 2 ; Tamás Bartók 2 ; Roy Dakroub 2 and Gergely Kocsis 3

Affiliations: 1 University of Debrecen, Doctoral School of Informatics, Hungary ; 2 EPAM Systems, Hungary ; 3 University of Debrecen, Faculty of Informatics, Department of Informatics Systems and Networks, Hungary

Keyword(s): Generative Artificial Intelligance (GenAI), Large Language Models (LLM), Industry, Education, Productivity.

Abstract: Generative AI tools are the cutting edge solutions of complex AI related problems. While investigating state-of-the-art results related to the effect of GenAI in the literature, one can note that the trends most likely lead to the expectation of a positive effect on the middle and long run. Based on these findings we define 4 productivity gain related hypotheses that we study using two types of methodologies. Namely we perform a survey research related to university-industry collaboration and quantitative studies mainly based on industrial productivity metrics. We have partnered with a major IT services provider - EPAM Systems - to be able to track, validate and analyze the key productivity metrics of software development projects, with and without using GenAI tools. This evaluation is being performed on various stages of the Software Development Lifecycle (SDLC) and on several project roles. Our goal is to measure the productivity increase provided by GenAI tools. Although this rese arch has just started recently, considering that the area has extremely high attention we present some initial findings. (More)

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Paper citation in several formats:
Héjja, F.; Bartók, T.; Dakroub, R. and Kocsis, G. (2024). Generative AI for Productivity in Industry and Education. In Proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-698-9; ISSN 2184-5034, SciTePress, pages 128-135. DOI: 10.5220/0012736200003708

@conference{complexis24,
author={Ferenc Héjja. and Tamás Bartók. and Roy Dakroub. and Gergely Kocsis.},
title={Generative AI for Productivity in Industry and Education},
booktitle={Proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2024},
pages={128-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012736200003708},
isbn={978-989-758-698-9},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - Generative AI for Productivity in Industry and Education
SN - 978-989-758-698-9
IS - 2184-5034
AU - Héjja, F.
AU - Bartók, T.
AU - Dakroub, R.
AU - Kocsis, G.
PY - 2024
SP - 128
EP - 135
DO - 10.5220/0012736200003708
PB - SciTePress

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