Introduction

Welcome to the fascinating world of Monitor-Guided Decoding (MGD) in code generation using Language Models! This article will walk you through the repository hosting the official code and data artifacts for the groundbreaking paper Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context presented at NeurIPS 2023. We delve into how MGD leverages static analysis to guide code generation and improve accuracy, ensuring that developers have the tools they need for successful code completion.

Repository Contents

The repository contains several important resources:

• Datasets: Including PragmaticCode and DotPrompts.
• Evaluation scripts: To evaluate LMs based on various metrics.
• Inference Results over DotPrompts: Reproducing graphs and tables documented in the paper.
• multilspy: A cross-platform library for simplified interaction with language servers.
• Monitor-Guided Decoding: Implementation of various monitors for static analysis.

Monitor-Guided Decoding: Motivating Example

Imagine a diligent coder tasked with completing a piece of software. The coder needs to ensure that the identifiers being used are consistent with the expected types, much like a chef needing specific herbs to complete a recipe. Without having the recipe in hand—the equivalent of context in programming—the coder risks adding the wrong ingredients, resulting in a compilation error. This is where MGD shines! By using static analysis, MGD helps the language model (LM) understand the types it should use, guiding the generation of valid code. In our example, it successfully prevents the generation of non-existent identifiers and enhances the compilation rate by 19-25%, leading to a higher quality output without any fine-tuning required.

1. Datasets

The repository contains two key datasets:

PragmaticCode

A collection of real-world open-source Java projects aimed at providing a robust training ground for machine learning models. It includes:

• 100 repositories and their dependencies.
• Available for download on Zenodo.

DotPrompts

Derived from PragmaticCode, containing 10,538 method completion tasks for benchmarking language models.

2. Evaluation Scripts

Environment Setup

Make sure to set up your Python environment with the required dependencies. We recommend Python 3.10 for compatibility.

setuppython3 -m venv venv_monitors4codegen
source venv_monitors4codegen/bin/activate

Running the Evaluation Script

Once everything is in place, you can run the evaluation script to analyze your results:

python3 eval_results.py path_to_inference_results.csv path_to_PragmaticCode_file_contents.json path_to_output_directory

3. Inference Results Over DotPrompts

All inferences generated from various model configurations are cataloged within the provided CSV file format. To analyze and reproduce figures and tables:

python3 eval_results.py inference_results_dotprompts_results.csv datasets/PragmaticCode_fileContentsByRepo.json results

4. multilspy

The multilspy library simplifies interaction with language servers. This boosts productivity by abstracting complex configurations, allowing for seamless querying of various static analyses.

Install it using:

pip install https://github.com/microsoft/multilspy/archive/main.zip

5. Monitor-Guided Decoding

MGD provides a powerful interface through which developers can monitor various properties during code generation. You can create monitors that enforce constraints such as type correctness and argument validity. Here’s how to utilize them effectively:

from multilspy import SyncLanguageServer
# Example code here...

Troubleshooting

If you encounter issues, such as an error concerning asyncio while executing tests for MGD, ensure you’re using Python 3.10. Check out the StackOverflow Discussion for potential fixes. For further assistance, do not hesitate to reach out.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.