Hear Your Code Fail, Voice-Assisted Debugging for Python
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908460266815488 |
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| author | Amiri, Sayed Mahbub Hasan Islam, Md. Mainul Hossen, Mohammad Shakhawat Amiri, Sayed Majhab Hasan Mamun, Mohammad Shawkat Ali Kabir, Sk. Humaun Akter, Naznin |
| author_facet | Amiri, Sayed Mahbub Hasan Islam, Md. Mainul Hossen, Mohammad Shakhawat Amiri, Sayed Majhab Hasan Mamun, Mohammad Shawkat Ali Kabir, Sk. Humaun Akter, Naznin |
| contents | This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugging, while enabling 78% faster error identification through vocalized exception classification and contextualization. The system achieves sub-1.2 second voice latency with under 18% CPU overhead during exception handling, vocalizing error types and consequences while displaying interactive tracebacks with documentation deep links. Criteria validate compatibility across Python 3.7+ environments on Windows, macOS, and Linux platforms. Needing only two lines of integration code, the plugin significantly boosts availability for aesthetically impaired designers and supports multitasking workflows through hands-free error medical diagnosis. Educational applications show particular promise, with pilot studies indicating 45% faster debugging skill acquisition among novice programmers. Future development will incorporate GPT-based repair suggestions and real-time multilingual translation to further advance auditory debugging paradigms. The solution represents a fundamental shift toward human-centric error diagnostics, bridging critical gaps in programming accessibility while establishing new standards for cognitive efficiency in software development workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hear Your Code Fail, Voice-Assisted Debugging for Python Amiri, Sayed Mahbub Hasan Islam, Md. Mainul Hossen, Mohammad Shakhawat Amiri, Sayed Majhab Hasan Mamun, Mohammad Shawkat Ali Kabir, Sk. Humaun Akter, Naznin Programming Languages Computation and Language This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugging, while enabling 78% faster error identification through vocalized exception classification and contextualization. The system achieves sub-1.2 second voice latency with under 18% CPU overhead during exception handling, vocalizing error types and consequences while displaying interactive tracebacks with documentation deep links. Criteria validate compatibility across Python 3.7+ environments on Windows, macOS, and Linux platforms. Needing only two lines of integration code, the plugin significantly boosts availability for aesthetically impaired designers and supports multitasking workflows through hands-free error medical diagnosis. Educational applications show particular promise, with pilot studies indicating 45% faster debugging skill acquisition among novice programmers. Future development will incorporate GPT-based repair suggestions and real-time multilingual translation to further advance auditory debugging paradigms. The solution represents a fundamental shift toward human-centric error diagnostics, bridging critical gaps in programming accessibility while establishing new standards for cognitive efficiency in software development workflows. |
| title | Hear Your Code Fail, Voice-Assisted Debugging for Python |
| topic | Programming Languages Computation and Language |
| url | https://arxiv.org/abs/2507.15007 |