Voice-Assisted Multimodal Debugging and Comparative Analysis of Python Applications in Visual Studio Code and PyCharm

Abstract

Software debugging is a crucial but lengthy process in software development that may require developers to decode complicated error messages and stack traces. The growing complexity of Python applications today underscores the need for more intuitive and effective debugging methods. This paper focuses on enhancing debugging effectiveness by presenting a voice-assisted multimodal debugging system. This work is valuable because it helps reduce cognitive load and improve accessibility, particularly for developers who have difficulty with traditional text-based debugging techniques. Current debugging tools in integrated development environments like Visual Studio Code and PyCharm are mostly visual, which can slow error understanding and increase cognitive load. These solutions are not multimodal and are not able to accommodate the needs of different users. The aim of the study is to develop and test a system that combines voice feedback and visual debugging to enhance understanding of errors and overall productivity. The suggested system will present a voice-assisted debugging system that records the runtime exceptions and provides audio and graphical feedback, as well as smart recommendations. Experimental evidence shows that the system is also faster at identifying errors and gives a more user-friendly debugging experience in both development environments. Also, the approach offers benefits such as lower cognitive load, greater accessibility, and increased resource awareness through comparisons of IDE performance.