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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63575
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Applications
Bapuji Institute of Engineering and Technology
Master of Computer Applications
Bapuji Institute of Engineering and Technology
With the increasing demand for automation and smart tools in programming a “Real-Time Code Companion” can assist developers by converting spoken instructions into executable code while providing natural language explanations system that interprets voice commands generates code in real time and explains the logic behind it and executes the displayed code in online compiler based on language and helping programmers learn debug or write code faster to capture user input and convert it into text. The text is and AI models trained on programming concepts to generate accurate code snippets. Simultaneously the system produces what the code does improving understanding and making it easier for beginners or even experienced developers to follow. By integrating voice recognition code generation and explanation modules the system provides a hands-free interactive programming environment. It can assist in multiple programming languages and frameworks making it a versatile tool for coding education rapid prototyping and software development. The project demonstrates how AI and voice interfaces can enhance developer productivity learning and accessibility in programming.
KEYWORDS
Indeed the two major objectives defined concerning the Real-Time Code Companion are met in this version. These are immediate coding and voice explanations. However due to modern technological advancements there is room for improvement in future developments. This may include supporting programming languages. Experimental results shows that the model achieves an accuracy of approximately 88%–94%. Smart automation in the identification and correction of coding faults in programming would be ideal assistance to the programmer. Modern approaches in AI would make coding more contextually correct. Modern approaches in AI are better equipped to deal with tough programming rules. By improving speech recognition the system could be more adaptable to various accents and speaking speeds.
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Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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