自动关联多源错误并生成人话解释，让技术问题变易懂。
Automated Multi-Source Debugging and Natural Language Error Explanation for Dashboard Applications
- 整合浏览器、API、服务器日志等多源数据进行自动关联
- 利用大模型将复杂错误转为自然语言说明，提升可读性
- 适合运维和产品团队快速定位问题，降低用户困惑
现代Web仪表盘与企业应用越来越多依赖复杂的分布式微服务架构。尽管这种架构具备可扩展性，但也带来了显著的调试与可观测性挑战。当故障发生时，用户通常只看到如“Something went wrong”这类模糊提示，掩盖了真实根因——可能来自前端异常、API契约违规或后端逻辑错误。现有监控工具虽能独立捕获事件，但难以有效关联，也无法向非技术人员提供清晰解释。本文提出一种自动化多源调试与自然语言错误解释系统，可实时收集并关联来自浏览器、API、服务器日志的数据，验证API契约，并利用大语言模型生成自然语言解释。该方法显著缩短了支持工程师的平均修复时间，同时通过将晦涩错误码转化为可操作洞察，提升了用户体验。
原文摘要 · Abstract (English)
Modern web dashboards and enterprise applications increasingly rely on complex, distributed microservices architectures. While these architectures offer scalability, they introduce significant challenges in debugging and observability. When failures occur, they often manifest as opaque error messages to the end-user such as Something went wrong. This masks the underlying root cause which may reside in browser side exceptions, API contract violations, or server side logic failures. Existing monitoring tools capture these events in isolation but fail to correlate them effectively or provide intelligible explanations to non technical users. This paper proposes a novel system for Automated Multi Source Debugging and Natural Language Error Explanation. The proposed framework automatically collects and correlates error data from disparate sources such as browser, API, server logs and validates API contracts in real time, and utilizes Large Language Models to generate natural language explanations. This approach significantly reduces Mean Time to Resolution for support engineers and improves the user experience by transforming cryptic error codes into actionable insights.
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