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

Source: arXiv - 2602.15362v1

Overview

Modern dashboards built on micro‑service back‑ends generate errors that are hard to trace—users see generic messages like “Something went wrong,” while the real fault may be hidden in a browser console, an API contract breach, or a server‑side exception. The paper by Tata and Rajhans introduces an end‑to‑end system that automatically gathers error signals from all these sources, stitches them together, validates API contracts on the fly, and then uses a large language model (LLM) to turn the technical dump into a clear, human‑readable explanation. The result is a measurable drop in mean‑time‑to‑resolution (MTTR) for support teams and a smoother experience for end‑users.

Key Contributions

• Unified multi‑source error collector that streams logs from browsers, API gateways, and server processes into a single correlation engine.
• Real‑time API contract validator that detects mismatches between request/response payloads and OpenAPI/GraphQL schemas as errors occur.
• LLM‑driven natural‑language explainer that synthesizes correlated data into concise, user‑friendly messages (e.g., “The dashboard failed because the inventory service returned a 500 error while the client expected a list of products”).
• Empirical evaluation showing up to a 42 % reduction in MTTR on a production‑grade dashboard platform compared with conventional monitoring stacks.
• Open‑source prototype (available on GitHub) that can be plugged into existing observability pipelines (e.g., OpenTelemetry, Elastic APM).

Methodology

1. Instrumentation & Data Ingestion – The authors instrumented a sample dashboard with lightweight agents that push browser console events, HTTP request/response traces, and server‑side logs to a central Kafka topic.
2. Correlation Engine – Using a combination of request IDs, timestamps, and causal graph construction, the engine groups events that belong to the same user interaction.
3. Contract Validation Layer – Every API call is checked against its OpenAPI spec; violations (missing fields, type mismatches) are flagged as “contract errors.”
4. Prompt Engineering for LLM – The correlated error bundle is formatted into a structured prompt (error type, stack trace snippets, contract violation details) and fed to a GPT‑4‑style model fine‑tuned on a curated dataset of developer‑written explanations.
5. Explanation Generation & Ranking – The model returns several candidate explanations; a lightweight ranking model selects the most actionable one based on relevance and readability scores.
6. User‑Facing Integration – The final explanation is surfaced either to a support ticketing system (for engineers) or directly to the UI as a friendly tooltip (for end users).

Results & Findings

The authors also performed a qualitative survey with 30 support engineers: 87 % said the natural‑language explanations helped them triage faster, and 73 % felt more confident communicating with non‑technical stakeholders.

Practical Implications

• Faster Incident Response – By surfacing the root cause instantly, on‑call engineers can skip the manual log‑digging step, which translates to cost savings and higher SLA compliance.
• Improved Customer Experience – End users receive meaningful error messages instead of “Something went wrong,” reducing frustration and support ticket volume.
• Lower Barrier to Adoption of Micro‑services – Teams can confidently roll out more granular services knowing that cross‑service failures will be automatically correlated and explained.
• Plug‑and‑Play Observability – The prototype integrates with existing OpenTelemetry collectors, meaning organizations can adopt the approach without a full rewrite of their monitoring stack.
• Knowledge Capture – The generated explanations become a living knowledge base that can be reused for onboarding, documentation, and automated incident post‑mortems.

Limitations & Future Work

• LLM Dependence – The quality of explanations hinges on the underlying language model; edge cases with obscure stack traces sometimes produce vague output.
• Scalability of Correlation – In extremely high‑throughput environments (millions of requests per second), the current Kafka‑based pipeline may need sharding or a more distributed graph engine.
• Security & Privacy – Sending raw logs to an LLM (especially a hosted API) raises data‑privacy concerns; the authors suggest on‑premise fine‑tuning as a mitigation.
• Domain Generalization – The evaluation focused on a single dashboard product; future work will test the framework across varied domains (e.g., IoT dashboards, fintech trading consoles).
• Feedback Loop – Incorporating engineer feedback to continuously improve the ranking model is planned but not yet implemented.

Overall, the paper presents a compelling blend of observability engineering and AI‑driven natural language generation that could reshape how modern web applications handle errors in production.

Authors

• Devendra Tata
• Mona Rajhans

Paper Information

• arXiv ID: 2602.15362v1
• Categories: cs.SE, cs.AI
• Published: February 17, 2026
• PDF: Download PDF