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README

🏟️ Long Code Arena Baselines

What is Long Code Arena?

Long Code Arena is a suite of benchmarks for code-related tasks with large contexts, up to a whole code repository. It currently spans six different tasks and contains six datasets: * 🤗 Library-based code generation * 🤗 CI builds repair * 🤗 Project-level code completion * 🤗 Commit message generation * 🤗 Bug localization * 🤗 Module summarization

Where are the baselines?

For each task, we have different approaches and environments. You can find the baseline solutions and the necessary information about each task in the corresponding directories.

How can I submit my results?

We are excited to invite you to participate in solving our benchmarks! To submit your results, please send the following materials to our 📩 email (lca@jetbrains.com):
Results: Include the summary of your benchmark outcomes. * Reproduction Package: To ensure the integrity and reproducibility of your results, please include the code for context collection (if any), generation of predictions, and evaluating. You can follow our baselines as a reference.
Metadata: Model information, organization name, licence of your model, context size, and other information you find relevant.

Core symbols most depended-on inside this repo

open
called by 123
library_based_code_generation/results/codellama/CodeLlama-7b-Instruct-hf/128.py
save
called by 93
library_based_code_generation/results/mistralai/Mistral-7B-Instruct-v0.3/142.py
run
called by 79
library_based_code_generation/results/gpt-4-0125-preview/108.py
chat_message
called by 75
library_based_code_generation/results/codellama/CodeLlama-70b-Instruct-hf/22.py
run
called by 72
library_based_code_generation/results/mistralai/Mistral-7B-Instruct-v0.3/13.py
render
called by 69
library_based_code_generation/results/bm25/mistralai/Mistral-7B-Instruct-v0.3/6.py
open
called by 66
library_based_code_generation/results/bm25/codellama/CodeLlama-7b-Instruct-hf/128.py
solve
called by 53
library_based_code_generation/results/mistralai/Mistral-7B-Instruct-v0.3/138.py

Shape

Function 1,921
Method 1,597
Class 568
Route 39

Languages

Python100%
Java1%
Kotlin1%

Modules by API surface

library_based_code_generation/results/bm25/mistralai/Mixtral-8x7B-Instruct-v0.1/128.py37 symbols
project_level_code_completion/eval/line_generators.py35 symbols
library_based_code_generation/results/mistralai/Mixtral-8x7B-Instruct-v0.1/6.py34 symbols
library_based_code_generation/results/bm25/mistralai/Mistral-7B-Instruct-v0.3/128.py31 symbols
library_based_code_generation/results/bm25/codellama/CodeLlama-7b-Instruct-hf/128.py31 symbols
library_based_code_generation/results/mistralai/Mistral-7B-Instruct-v0.3/128.py30 symbols
library_based_code_generation/results/bm25/mistralai/Mistral-7B-Instruct-v0.3/6.py28 symbols
library_based_code_generation/results/bm25/gpt-3.5-turbo-0125/128.py28 symbols
library_based_code_generation/results/bm25/codellama/CodeLlama-7b-Instruct-hf/49.py28 symbols
library_based_code_generation/results/gpt-4-0125-preview/128.py27 symbols
library_based_code_generation/results/gpt-3.5-turbo-0125/128.py27 symbols
library_based_code_generation/results/bm25/gpt-4-0125-preview/128.py27 symbols

For agents

$ claude mcp add lca-baselines \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact