Authors :
Rishita Chaubey; Manish Kumar; Manish Kumar Shah; Hemant Sonavane; Manisha Chandramaully
Volume/Issue :
Volume 9 - 2024, Issue 10 - October
Google Scholar :
https://tinyurl.com/3r4u95ta
Scribd :
https://tinyurl.com/2s34ux4k
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24OCT1010
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The increasing use of computing devices brings
convenience but also challenges. Traditional interfaces
can lead to repetitive strain, slow interactions, and
accessibility problemsfor software developers, who often
find it hard to turn complex ideas into code, especially
with unfamiliar APIs. In response, there is a growing
interest in automation and smart machines that act like
humans. However, a major obstacle remains—the gap in
human-machine interaction.
Introducing “RISH Assistance,” a voice-activated
digital assis- tant designed for developers. RISH enables
users to navigate tasks more efficiently, reducing
reliance on screens and tra- ditional input methods.
Utilizing advanced voice recognition and Natural
Language Processing (NLP), RISH transforms the
developer experience by providing easy access to
resources, seamless code suggestions, and natural
troubleshooting—all through voice commands.
Additionally, it can manage phone functions, allowing
calls and messages directly from the desktop interface,
enhancing productivity. Security is ensured with face
authentication, and current session chat history tracking
allowsusers to revisit previous interactions.
RISH boosts productivity through hands-free
interaction, en- abling developers to focus on creative
problem-solving. It im- proves accessibility by offering
an alternative input method and streamlines workflows,
reducing cognitive load while fostering a more intuitive
development environment. By facilitating natural and
efficient engagement with machines, RISH Assistance
aims to enhance human-machine collaboration and
create a more inclusive software development
landscape.
Keywords :
Voice-Activated Assistant, Natural Language Processing, Developer Productivity, Accessibility, Automation, Human-Machine Interaction, Software Development Tools, Hands-Free Operation.
References :
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- Brain stroke prediction using ANN, 2021. Voice assistant using Python.
- Boris Beizer. Software Testing Techniques. Van Nostrand Reinhold, New York, NY, USA, 2nd edition, 1990.
- Ujjwal Gupta, Utkarsh Jindal, Apurv Goel, and Vaishali Malik. Desktop voice assistant. International Journal for Research in Applied Science Engineering Technology (IJRASET), 2022.
- Glenford J. Myers. The art of software testing. Communications of the ACM, 22(9):690–700, 1979.
- Ian Sommerville. Software Engineering. Addison-Wesley, 9th edition, 2011.
- S Subhash, Prajwal N Srivatsa, S Siddesh, A Ullas, and B Santhosh. Artificial intelligence-based voice assistant. In 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pages 593–596. IEEE, 2020.
- PlantText Team. Planttext: A free online plantuml editor. https://www.planttext.com, 2024. Accessed: 2024-10-11.
- Frank F Xu, Bogdan Vasilescu, and Graham Neubig. In-ide code generation from natural language: Promise and challenges. ACM Transactions on Software Engineering and Methodology (TOSEM), 31(2):1–47, 2022.
- ”Voice Command Recognition for Smart Assistants”, A. Kumar and S. Sharma, International Journal of Computational Intelligence Systems, vol. 14, no. 3, 2021.
- ”Natural Language Processing with Hugging Face”, J. Smith and M. Doe, Journal of Artificial Intelligence Research, vol. 57, 2022.
- ”Text-to-Speech Systems: A Comprehensive Review”, T. Verma, Jour- nal of Speech and Audio Processing, vol. 33, no. 2, 2020.
- ”Real-Time Face Recognition Techniques”, L. Zhang and M. Shah, Proc. ACM Conf. on Multimedia, 2022
The increasing use of computing devices brings
convenience but also challenges. Traditional interfaces
can lead to repetitive strain, slow interactions, and
accessibility problemsfor software developers, who often
find it hard to turn complex ideas into code, especially
with unfamiliar APIs. In response, there is a growing
interest in automation and smart machines that act like
humans. However, a major obstacle remains—the gap in
human-machine interaction.
Introducing “RISH Assistance,” a voice-activated
digital assis- tant designed for developers. RISH enables
users to navigate tasks more efficiently, reducing
reliance on screens and tra- ditional input methods.
Utilizing advanced voice recognition and Natural
Language Processing (NLP), RISH transforms the
developer experience by providing easy access to
resources, seamless code suggestions, and natural
troubleshooting—all through voice commands.
Additionally, it can manage phone functions, allowing
calls and messages directly from the desktop interface,
enhancing productivity. Security is ensured with face
authentication, and current session chat history tracking
allowsusers to revisit previous interactions.
RISH boosts productivity through hands-free
interaction, en- abling developers to focus on creative
problem-solving. It im- proves accessibility by offering
an alternative input method and streamlines workflows,
reducing cognitive load while fostering a more intuitive
development environment. By facilitating natural and
efficient engagement with machines, RISH Assistance
aims to enhance human-machine collaboration and
create a more inclusive software development
landscape.
Keywords :
Voice-Activated Assistant, Natural Language Processing, Developer Productivity, Accessibility, Automation, Human-Machine Interaction, Software Development Tools, Hands-Free Operation.
