TriageFlow: Intelligent Bug Intake & Triage Assistant

Problem Statement

Modern engineering teams often struggle with poorly structured bug reports that lack critical context, leading to slow triage cycles and "ping-pong" communication between QA and developers. Identifying the specific failure layer (Frontend, Backend, Database, etc.) and generating a reliable reproduction path is often a manual, time-consuming process.

Solution Overview

TriageFlow is a professional engineering tool designed to automate the initial stages of bug triage. It uses a hybrid architecture combining a deterministic rule-based inference engine with an optional AI enhancement layer (Gemini) to classify bugs, calculate confidence scores, and generate actionable reproduction plans and debugging checklists.

Key Features

• Intelligent Layer Prediction: Automatically maps bug reports to Frontend, Backend, Database, Authentication, or Network layers.
• Layer Score Breakdown: Provides a transparency-first view of scoring across all potential failure points.
• Context-Aware Reproduction Plans: Generates step-by-step instructions tailored to the predicted failure layer.
• Dynamic Confidence Adjustment: Adjusts confidence scores based on agreement between rule-based logic and AI hypotheses.
• Engineering Handoff View: Generates a clean, minimal technical report optimized for developer handoff and documentation.
• Risk Classification: Categorizes issues by risk level based on signals in the error message and description.

Architecture

TriageFlow employs a Rule-Based + AI Hybrid approach:

1. Inference Engine: A deterministic engine scans for technical signals (keywords, patterns, stack trace signatures) to provide a baseline prediction.
2. AI Assistant: An optional Gemini-powered layer provides deep analysis, alternative hypotheses, and refined reproduction steps.
3. Safety Layer: The AI is designed to complement, not blindly override, the deterministic engine. Confidence scores are adjusted explainably based on cross-validation.

Local Setup

1. Clone the repository:

   git clone https://github.com/your-repo/triageflow.git
   cd triageflow

2. Install dependencies:

   npm install

3. Configure Environment: Create a .env file and add your Gemini API key:

   GEMINI_API_KEY=your_api_key_here

4. Start the application:

   npm run dev

Example Usage

• Input: "User cannot log in, receiving 401 Unauthorized after entering correct credentials."
• Analysis: TriageFlow identifies "401" and "log in" as high-confidence signals for the Authentication layer.
• Output: A structured reproduction plan focusing on token validation and session headers, plus a debugging checklist for auth middleware.

Limitations

• Rule-based detection depends on the breadth of the signal library.
• AI insights require an active Gemini API connection.
• Currently optimized for web-based application stacks.

Future Roadmap

• Integration with Jira/GitHub Issues API.
• Custom signal library configuration for team-specific stacks.
• Support for mobile-specific failure layers.

Acknowledgments

This project was developed with AI-assisted collaboration. Planning, architectural discussions, and system design were supported through iterative discussions with ChatGPT, while implementation and code refinement were accelerated using Gemini. Final decisions, integration, and product direction were driven by the author.