Automated Multi-Source Debugging and Natural Language Error Explanation for Dashboard Applications

TL;DR

This paper introduces a system that automatically collects, correlates, and explains errors from multiple sources in web applications using large language models, improving debugging efficiency and user experience.

Contribution

It presents a novel framework combining multi-source error collection, real-time API validation, and natural language explanations powered by large language models.

Findings

Reduces mean time to resolution for debugging.

Transforms cryptic error messages into understandable explanations.

Enhances user experience with actionable insights.

Abstract

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.