2/18/2026 | USA | technology | ✓ Verified - arxiv.org

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

#automated debugging #multi‑source source aggregation #natural language error explanation #dashboard applications #microservices #root cause analysis #observability #distributed traceability

📌 Key Takeaways

Proposes an automated debugging tool that aggregates signals from multiple sources (logs, traces, browser errors) to identify root causes in microservice architectures.
Generates natural‑language explanations of errors for dashboard users, replacing opaque messages like "Something went wrong."
Designed for integration into modern web dashboards used by enterprises and developers.
Demonstrates how the system enhances observability and reduces time‑to‑resolution for complex distributed systems.
Aims to bridge the gap between technical details of failures and the non‑technical expectations of end‑users.
Provides a prototype implementation and evaluates its effectiveness on real‑world dashboard scenarios.

📖 Full Retelling

The authors, a team of computer scientists, have released a study that introduces an automated system for multi‑source debugging and natural language error explanation specifically designed for web dashboard applications. Their work, published as a preprint on arXiv in February 2026, tackles the growing difficulty of diagnosing faults in complex, distributed microservices frameworks that underpin modern enterprise dashboards. By automatically correlating logs, traces, and browser exceptions, the system isolates the root cause of errors and generates intelligible explanations for end‑users, ultimately improving transparency and user experience.

🏷️ Themes

Distributed systems, Microservices debugging, Observability, Natural language processing, User experience

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Deep Analysis

Why It Matters

This research addresses a critical pain point in modern web dashboards, where opaque error messages hinder rapid troubleshooting. By automating debugging across multiple sources and translating root causes into natural language, it can reduce downtime and improve developer productivity.

Context & Background

Microservices increase complexity
Error messages often lack detail
Current debugging relies on manual log analysis

What Happens Next

The authors plan to integrate their system into popular dashboard frameworks and evaluate its impact on mean time to resolution. Future work may extend the approach to mobile applications and real time monitoring.

Frequently Asked Questions

What is the main goal of the research?

To automatically identify root causes of errors in dashboard applications and explain them in plain language.

How does the system gather data?

It collects logs, stack traces, and API responses from the browser, server, and network layers.

Will this be available as an open source tool?

The authors intend to release the prototype under an open source license after the conference presentation.

Original Source

              arXiv:2602.15362v1 Announce Type: cross 
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 violat
            

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Source

arxiv.org