Execution-based evaluation of code in 10+ languages

This repository contains code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data, namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval. Results and findings can be found in the paper "Multi-lingual Evaluation of Code Generation Models" (https://arxiv.org/abs/2210.14868).

Paper summary

Our paper describes the language conversion framework, the synthetic solution generation, and many other types of evaluation beyond the traditional function completion evaluation such as translation, code insertion, summarization, and robustness evaluation.

Language conversion of execution-based function completion datasets

Below we demonstrate the language conversion (component A above) for the conversion from Python to Java (abridged example for brevity).

Installation

Check out and install this repository: git clone https://github.com/amazon-science/mxeval.git pip install -e mxeval

Dependencies

We provide scripts to help set up programming language dependencies that are used to execute and evaluate using datasets in MBXP.

Amazon Linux AMI

bash language_setup/amazon_linux_ami.sh

Ubuntu

bash language_setup/ubuntu.sh

Usage

This program exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. See the comment in execution.py for more information and instructions.

Each sample is formatted into a single line: {"task_id": "Corresponding task ID", "completion": "Completion only without the prompt", "language": "programming language name"} We provide data/mbxp/examples/mbxp_samples.jsonl to illustrate the format.

Here is nearly functional example code (you just have to provide generate_one_completion to make it work) that saves generated completions to samples.jsonl. ``` from mxeval.data import writejsonl, readproblems

problems = read_problems()

numsamplespertask = 200 samples = [ dict(taskid=taskid, language=problems[task_id]["language"], completion=generateonecompletion(problems[task_id]["prompt"])) for taskid in problems for _ in range(numsamplespertask) ] writejsonl("samples.jsonl", samples) ```

To evaluate the samples for, e.g., Java MBJP evaluation, run evaluate_functional_correctness data/mbxp/examples/mbjp_samples.jsonl --problem_file data/mbxp/mbjp_release_v1.jsonl or to run all languages for lang in mbcpp mbcsp mbgp mbjp mbjsp mbkp mbphp mbplp mbpp mbrbp mbscp mbswp mbtsp; do evaluate_functional_correctness --problem_file data/mbxp/${lang}_release_v1.jsonl data/mbxp/examples/${lang}_samples.jsonl; done You can check the programming-language dependency installation by running the above example for each MBXP dataset. You should obtain the following results for the mbxp_samples.jsonl files provided:

| Dataset | pass@1 | |---------|--------| | MBCPP | 79.60% | | MBCSP | 63.63% | | MBGP | 39.19% | | MBJP | 85.30% | | MBJSP | 78.67% | | MBKP | 63.77% | | MBPHP | 72.77% | | MBPLP | 38.41% | | MBPP | 82.24% | | MBRBP | 58.90% | | MBSCP | 42.96% | | MBSWP | 29.40% | | MBTSP | 87.29% |

Note: Because there is no unbiased way of estimating pass@k when there are fewer samples than k, the script does not evaluate pass@k for these cases. To evaluate with other k values, pass --k <comma-separated-values-here>. For other options, see $ evaluate_functional_correctness --help However, we recommend that you use the default values for the rest.

Example usage with non-default values

evaluate_functional_correctness data/mbxp/samples/mbjp_samples.jsonl --problem_file data/mbxp/mbjp_release_v1.jsonl --n_workers 63 --k 1,5,10,100

Known Issues

While evaluation uses very little memory, you might see the following error message when the system is running out of RAM. Since this may cause some correct programs to fail, we recommend that you free some memory and try again. malloc: can't allocate region

Some system might require longer compilation timeout. If you see that the execution fails due to compilation timeout reason, this number should be increased accordingly.

Canonical solutions release

We have released canonical solutions for certain popular langauges (v1.2). The detailed numbers of the solutions for each langauge are listed below.

| Dataset | # solutions | # problems | |---------|-------------|------------| | MBCPP | 773 | 848 | | MBCSP | 725 | 968 | | MBJP | 874 | 966 | | MBJSP | 938 | 966 | | MBKP | 796 | 966 | | MBPHP | 950 | 966 | | MBPP | 960 | 974 | | MBRBP | 784 | 966 | | MBTSP | 967 | 968 |

Future release

We plan to release synthetic canonical solutions as well as processed datasets for other evaluation tasks such as code-insertion, code-translation, etc.

Credits

We adapted OpenAI's human-eval package (https://github.com/openai/human-eval) for the multi-lingual case. We thank OpenAI for their pioneering effort in this field including the release of the original HumanEval dataset, which we convert to the multi-lingual versions. We also thank Google for their release of the original MBPP Python dataset (https://github.com/google-research/google-research/tree/master/mbpp), which we adapt and convert to other programming languages.

Citation

Please cite using the following bibtex entry:

``` @article{mbxp_athiwaratkun2022, title = {Multi-lingual Evaluation of Code Generation Models}, author = {Athiwaratkun, Ben and Gouda, Sanjay Krishna and Wang, Zijian and Li, Xiaopeng and Tian, Yuchen and Tan, Ming and Ahmad, Wasi Uddin and Wang, Shiqi and Sun, Qing and Shang, Mingyue and Gonugondla, Sujan Kumar and Ding, Hantian and Kumar, Varun and Fulton, Nathan and Farahani, Arash and Jain, Siddhartha and Giaquinto, Robert and Qian, Haifeng and Ramanathan, Murali Krishna and Nallapati, Ramesh and Ray, Baishakhi and Bhatia, Parminder and Sengupta, Sudipta and Roth, Dan and Xiang, Bing}, doi = {10.48550/ARXIV.2210.14868}, url = {https://arxiv.org/abs/2210.14868}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} }

```