LARCH: Large Language Model-based Automatic Readme Creation with Heuristics

by   Yuta Koreeda, et al.

Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatically creating one remains a challenge even with the recent advancements in large language models (LLMs), because it requires generating an abstract description from thousands of lines of code. In this demo paper, we show that LLMs are capable of generating a coherent and factually correct readmes if we can identify a code fragment that is representative of the repository. Building upon this finding, we developed LARCH (LLM-based Automatic Readme Creation with Heuristics) which leverages representative code identification with heuristics and weak supervision. Through human and automated evaluations, we illustrate that LARCH can generate coherent and factually correct readmes in the majority of cases, outperforming a baseline that does not rely on representative code identification. We have made LARCH open-source and provided a cross-platform Visual Studio Code interface and command-line interface, accessible at A demo video showcasing LARCH's capabilities is available at


Benchmarking Large Language Models for Automated Verilog RTL Code Generation

Automating hardware design could obviate a significant amount of human e...

SoTaNa: The Open-Source Software Development Assistant

Software development plays a crucial role in driving innovation and effi...

AskIt: Unified Programming Interface for Programming with Large Language Models

In the evolving landscape of software development, Large Language Models...

Mobile-Env: A Universal Platform for Training and Evaluation of Mobile Interaction

The interaction platform plays a crucial role in the recent advancement ...

Sorrel: an IDE Plugin for Managing Licenses and Detecting License Incompatibilities

Software development is a complex process that includes many different t...

QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models

We present ongoing work on a new automatic code generation approach for ...

Please sign up or login with your details

Forgot password? Click here to reset