Long Code Arena: a Set of Benchmarks for Long-Context Code Models
Authors: name: Egor Bogomolov; name: Aleksandra Eliseeva; name: Timur Galimzyanov; name: Evgeniy Glukhov; name: Anton Shapkin; name: Maria Tigina; name: Yaroslav Golubev; name: Alexander Kovrigin; name: Arie van Deursen; name: Maliheh Izadi; name: Timofey Bryksin
Published: 2024-06-17 14:58:29

Abstract: Nowadays, the fields of code and natural language processing are evolving
rapidly. In particular, models become better at processing long context windows
- supported context sizes have increased by orders of magnitude over the last
few years. However, there is a shortage of benchmarks for code processing that
go beyond a single file of context, while the most popular ones are limited to
a single method. With this work, we aim to close this gap by introducing Long
Code Arena, a suite of six benchmarks for code processing tasks that require
project-wide context. These tasks cover different aspects of code processing:
library-based code generation, CI builds repair, project-level code completion,
commit message generation, bug localization, and module summarization. For each
task, we provide a manually verified dataset for testing, an evaluation suite,
and open-source baseline solutions based on popular LLMs to showcase the usage
of the dataset and to simplify adoption by other researchers. We publish the
benchmark page on HuggingFace Spaces with the leaderboard, links to HuggingFace
Hub for all the datasets, and link to the GitHub repository with baselines:
https://huggingface.co/spaces/JetBrains-Research/long-code-arena.