Directories

bench

The benchmark tables for test and dev sets accompanied by json-files with links from every function to functions that it calls (next).

data_extraction

The pipeline used for collecting the benchmark.

• tables contains tables with test coverage, json-files with function call graphs, repository statistics and a docstring labeling judgement,
• commit_dates.py extracts commit dates for functions from repositories,
• db_stat.py collects a table of functions from a function call graph,
• merge_commits.py merges commit dates into the tables,
• pipeline.sh runs aforementioned steps to produce a table for repository,
• merge_test_cov.py merges test coverage hits into the tables,
• generate_benchmark.py script produces benchmark and dev tables.

finetune

Notebooks and dockerfiles used to fine-tune models on training set. Training script is based on fsdp_qlora project.

streamlit_app

A streamlit took to evaluate models and visualize generated and original code as well as result tables. The directory contains a separate README file with instructions to run.

train_data

• prev contains json-files with links from every function to functions that call it, produced from function call graphs,
• train_functions contains tables of all train functions (similar to tables in bench) used for fine-tuning,
• db_stat_prev.py script produces prev tables,
• generate.py script produces train tables.

Cite

YABLoCo: Yet Another Benchmark for Long Context Code Generation