failtrace 0.7.3

Leveraging Large Language Models for Automated Test Results Analysis

FailTrace

Leveraging Large Language Models for Automated Test Results Analysis

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🌍 Overview

FailTrace helps developers and QA engineers analyze automated test results with Large Language Models (LLMs).
It builds dependency graphs, maps them with test execution logs, and generates interactive HTML reports with insights into failures.

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✨ Features

• Supports Python, Java, C#
• Dependency graph visualization (PyVis + NetworkX)
• Test log parsing (Pytest, JSON, TRX, …)
• Interactive reports (HTML + CSS + JS)
• Dry-run mode (no API calls)
• CLI-first design for CI/CD

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📦 Installation

pip install failtrace

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⚡ Quickstart

Run the full pipeline:

python -m failtrace full -p ./your-source-code -l ./your-test-results.xml --open-report

Run in quick mode (reuse cached graph):

failtrace quick -p ./src -l ./results.json

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📜 License

Licensed under the MIT License.

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👩‍💻 Author

Mohadese Akhoondy