3 research outputs found
LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
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
https://github.com/hitachi-nlp/larch. A demo video showcasing LARCH's
capabilities is available at https://youtu.be/ZUKkh5ED-O4.Comment: This is a pre-print of a paper accepted at CIKM'23 Demo. Refer to the
DOI URL for the original publicatio