Impact of AI Tools in Software Engineering: Boon or a Bane

Year : 2024 | Volume :11 | Issue : 01 | Page : 14-23
By

Bijee Lakshman

Abhinav S.

  1. Assistant Professor Department of Data Science, Women’s Christian College, Chennai Tamil Nadu India
  2. Student Computer Science and Business Systems Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai Tamil Nadu India

Abstract

Artificial Intelligence (AI) has become a transformative force, revolutionizing diverse sectors by integrating intelligent systems into everyday processes. Natural Language Processing (NLP) plays a crucial role, enabling machines to understand and produce human language, marking a significant advancement in technology. This innovation has various applications, including chatbots, language translation, and sentiment analysis, thereby improving interactions between humans and computers and facilitating information processing. Generative AI, a subset of AI, takes innovation to new heights by enabling machines to autonomously create content. This advancement is particularly evident in language models that exhibit context-aware content generation, revolutionizing creativity in various fields such as writing and art. The synergy between NLP and generative AI has paved the way for unprecedented advancements, showcasing the potential for machines to understand and generate contextually relevant content. In the realm of AI tools, a critical player is Codeium, an open-source code editor. Designed with software developers in mind, Codeium incorporates AI-powered functionalities such as smart code suggestions and syntax highlighting. This study describes how Codeium significantly enhances the efficiency of developers, facilitating smoother code writing, editing, and debugging processes. A comparative analysis with few other AI tools is made highlighting few demos of prompt engineering in Codeium. As the collective impact of AI, NLP, generative AI, and advanced tools like Codeium, continues to unfold, these technologies not only redefine the boundaries of what is achievable but also underscore their pervasive influence across industries. From language understanding to innovative content creation and streamlined software development, the multifaceted applications of these technologies underscore their significance in shaping the future of artificial intelligence.

Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Generative AI (Gen AI), Codeium, prompt engineering

[This article belongs to Journal of Software Engineering Tools & Technology Trends(josettt)]

How to cite this article: Bijee Lakshman, Abhinav S.. Impact of AI Tools in Software Engineering: Boon or a Bane. Journal of Software Engineering Tools & Technology Trends. 2024; 11(01):14-23.
How to cite this URL: Bijee Lakshman, Abhinav S.. Impact of AI Tools in Software Engineering: Boon or a Bane. Journal of Software Engineering Tools & Technology Trends. 2024; 11(01):14-23. Available from: https://journals.stmjournals.com/josettt/article=2024/view=140133


References

  1. Bertalan Meskó. Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial. J Med Internet Res. 2023 Oct; 25: e50638.
  2. Cheng Peng, Xi Yang, Aokun Chen, Smith Kaleb E, Nima Pour Nejatian, Costa Anthony B. A study of generative large language model for medical research and healthcare. NPJ Digit Med. 2023; 6(1): 210.
  3. Zhihan Lv. Generative artificial intelligence in the metaverse era. Cognitive Robotics. 2023; 3: 208–217.
  4. Venkatesh V. A Research Agenda grounded in UTAUT- Adoption and use of AI Tools. Ann Oper Res. 2022 Jan; 308(1): 641–652. https://doi.org/10.1007/s10479-020-03918-9.
  5. Mika Saari, Petri Rantanen, Mikko Nurminen, Terhi Kilamo, Kari Systä, Pekka Abrahamsson. Survey of AI Tool Usage in Programming Course: Early Observations. Agile Processes in Software Engineering and Extreme Programming – Workshops. 2023 Dec; 182–191.
  6. Olaf Zawacki-Richter, Marín Victoria I, Melissa Bond, Franziska Gouverneur. Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int J Educ Technol High Educ. 2019; 16(1): 39.
  7. Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh. Natural Language Processing- State of art, current trends and challenges. Multimed Tools Appl. 2023; 82(3): 3713–3744. doi: 10.1007/s11042-022-13428-. Epub 2022 Jul 14.
  8. Sabit Ekin. Prompt Engineering For ChatGPT: A Quick Guide To Techniques, Tips, And Best Practices. TechRxiv. May 04, 2023. DOI: 10.36227/techrxiv.22683919.v2.
  9. Naveed H, Khan AU, Qiu S, Saqib M, Anwar S, Usman M, Barnes N, Mian A. A comprehensive overview of large language models. arXiv preprint arXiv:2307.06435. 2023 Jul 12. doi: https://doi.org/10.48550/arXiv.2307.06435.
  10. Collobert R, Weston J, Com J, Karlen M, Kavukcuoglu K, Kuksa P. Natural Language Processing (Almost) from Scratch. J Mach Learn Res. 2011;12:2493–2537.
Regular Issue Subscription Review Article
Volume 11
Issue 01
Received February 26, 2024
Accepted March 21, 2024
Published April 5, 2024
Views: 46