Project Overview
LLMs are used to automatically generate contextually accurate unit tests to improve code quality and development efficiency.
Layman's Explanation
Adyen integrates AI to help developers create reliable code by generating unit tests, a critical step to ensure each piece of software works as intended.
Analogy
Creating unit tests is like running a safety check on each gear of a machine to ensure everything functions together smoothly. Adyen’s LLM integration automates this process, making it faster and more precise.
Details
Adyen uses Large Language Models (LLMs) to assist in generating unit tests for their codebase. Traditional LLMs, like those used in Github Copilot, typically handle isolated functions, but Adyen has developed a solution that understands broader system architecture and interdependencies. By combining Retrieval-Augmented Generation (RAG) with fine-tuning, their system provides developers with relevant code snippets and context-aware tests that support consistent code quality. This integration ensures that LLMs consider the full scope of code interactions, dependencies, and system-wide coherence, addressing the unique challenges in complex code environments.
Project Novelty
This approach combines LLMs, fine-tuning, and RAG specifically to handle complex codebase contexts, addressing a unique need in automated code quality management.
Project Estimates
Timeline:
8 months
Cost:
$200,000
Headcount:
4
