Microsoft’s CodePlan: Unleashing the Power of Language Models for Repository-Level Coding Tasks
The incredible generative capabilities of Large Language Models (LLMs) have ushered in a new era of automation in coding tasks. Applications like Amazon Code Whisperer, GitHub Copilot, and Replit have become ubiquitous, harnessing LLMs to effortlessly complete code segments or execute code modifications based on natural language instructions.
Despite their remarkable performance, these tools encounter challenges when tackling repository-level coding tasks. These tasks involve extensive code alterations across entire codebases, such as package migration or the addition of type annotations and other specifications.
In a recent paper, “CodePlan: Repository-level Coding using LLMs and Planning,” a team from Microsoft Research introduces CodePlan — a versatile framework designed to address the complexities of repository-level coding tasks, encompassing extensive code changes across large, interconnected codebases.
Problem Formalization: The team pioneers the formalization of the problem of automating repository-level coding tasks with LLMs, necessitating the analysis of code changes’ effects and their propagation throughout the repository.
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