Evaluating the Impact of ChatGPT and Advanced Language Models on Enhancing Low-Code and Robotic Process Automation
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Keywords:
ChatGPT, Advanced Language Models, Low-Code Development Platforms (LCDP), Robotic Process Automation (RPA), Natural Language Understanding, Human-Machine Collaboration, Automation Technologies, Digital TransformationAbstract
This study investigates the transformative potential of integrating advanced language models, specifically ChatGPT, into the realms of Low-Code Development Platforms (LCDPs) and Robotic Process Automation (RPA). As organizations continue to harness the power of automation to streamline workflows and bolster operational efficiency, the synergy between natural language understanding and automation technologies presents a compelling avenue for further advancements. The research explores how ChatGPT, a state-of-the-art language model, can facilitate a more intuitive and user-friendly interaction between developers and Low-Code platforms, thereby democratizing the application development process. The findings of this study contribute to a deeper understanding of the potential enhancements brought about by advanced language models in the context of Low-Code and RPA.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
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Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
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Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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