Possible Productivity Effects On Software Engineers by Advanced Artificial Intelligence


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Authors

  • Arsha Sheikhi HS of Endeavor-Austin, USA
  • Hamzah Raoof HS of Endeavor, Austin, USA
  • Zaid Khan HS of Endeavor-Austin, USA
  • Merve Gokgol HS of Endeavor, Austin-USA https://orcid.org/0009-0003-8162-6688

DOI:

https://doi.org/10.31039/plic.2024.11.265

Keywords:

AI, Artificial Intelligence, Productivity, Programming Jobs, Computer Science, Software Engineering

Abstract

Within the past few years, starting from the greater public use of AI from the recent “AI Boom,” ChatGPT or AI-Language Model equivalents have been making their way into software and other computer science-related work environments for developers and software engineers to use without significant financial cost. In this paper, we often mention the word “productivity,” so it is important to know how we measure this: we measure productivity in lines of code (LOC) to gauge the raw amount of coding done, bug resolutions done by developers to measure the reviewing of code, and customer satisfaction to measure the quality of the code, then combine all of these into “overall productivity.” In this paper, we will examine the effects on productivity that these Large Language Models (LLMs) have had on software engineering or other similar jobs.

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Published

2024-11-10

How to Cite

Arsha Sheikhi, Hamzah Raoof, Zaid Khan, & Gokgol, M. (2024). Possible Productivity Effects On Software Engineers by Advanced Artificial Intelligence. Proceedings of London International Conferences, (11), abs2. https://doi.org/10.31039/plic.2024.11.265

Issue

No. 11 (2024): Proceedings of London International Conference

Section

Abstracts

License

Copyright (c) 2024 Arsha Sheikhi, Hamzah Raoof, Zaid Khan, Merve Gokgol

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