Abstract
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.
Abstract (translated)
这项研究介绍了一种创新的语音辅助调试插件，专门用于Python编程语言。该插件将无声的运行时错误转化为可以操作的有声诊断信息。通过实现一个基于pyttsx3文本到语音转换和Tkinter基础GUI可视化的全局异常钩子架构，解决方案提供了多模态（同时使用听觉和视觉通道）的错误反馈。实证评估表明，与传统的堆栈跟踪调试相比，该方法可以减少37%的认知负荷（p<0.01, n=50），并且通过口头化异常分类和上下文信息，使错误识别速度提高了78%。 系统在处理异常时实现了不到1.2秒的语音延迟，并且CPU开销低于18%，同时能够用语音播报错误类型及后果，并显示带有文档深层链接的交互式堆栈跟踪。根据测试标准，在Windows、macOS和Linux平台上使用Python 3.7+环境进行了验证，证明了该插件具有良好的兼容性。 只需两行集成代码，此插件就能显著提高视觉障碍设计师的工作效率，并通过语音控制来支持多任务工作流程中的错误诊断。在教育应用方面，初步研究表明，在编程初学者中，使用这种技术可以让他们更快地掌握调试技能（45%的速度提升）。 未来的发展计划包括整合基于GPT的修复建议和实时多语言翻译功能，以进一步推动听觉调试方法的进步。这项解决方案代表了一种根本性的转变，即向以人为本的错误诊断方向发展，并弥合了编程可访问性方面的关键差距，同时为软件开发工作流程中的认知效率设立新的标准。
URL
https://arxiv.org/abs/2507.15007
