XVCLM: Revolutionizing Code Interpretation

I’ve built a language model called XVCLM that can read code and explain its functionality in natural language. This model works with various programming languages and is multilingual. In this blog, I’ll detail its capabilities and potential impacts.

Project XVCLM Overview
I developed XVCLM using roughly 1 million code-description pairs. The goal was simple: can machines describe code like humans do? While this is a challenging task, recent advancements in language models have made many difficult tasks more feasible.

Building language models for computer code has more challenges than traditional language models. First, the quality of data is often variable and less abundant. Publicly available datasets usually contain medium-quality code samples but lack quality descriptions. This can hurt the model’s performance early on and is hard to fix later. Second, achieving high performance in language models has become increasingly expensive. It’s well known that larger models with more trainable parameters usually perform better, but this comes with higher costs in terms of money and computing power.

Another significant challenge is ensuring the reliability of the system overall. Recent work from companies like OpenAI, Google, and Facebook has shown that language models can produce unexpected results, especially in language generation. Controlling what these models generate is still an open research problem.

Key Questions for Building XVCLM

With these challenges in mind, I focused on a few key questions to build a successful code description system:

1. Can I curate enough data samples with quality code descriptions?
2. How can I build a smaller language model that maximizes consistency and accuracy at the same time?

Building a Model for Describing Code

Model Architecture

To tackle these questions, XVCLM uses the well-known transformer architecture. I found that other architectures (like RNNs or GRUs) did not produce the same quality of results.

XVCLM consists of two models: XVCLM-MIN-DECT, which can only describe what is happening in the code, and XVCLM-LARGE (ALPHA+), which can generate code and function as a general-purpose language model for coding.

Dataset

Finding quality data was a major challenge. My goal with XVCLM is to provide natural summaries of code. Since there are no publicly available datasets with code samples and corresponding natural descriptions, I had to generate samples synthetically. I started by pre-training XVCLM on 885,000 publicly available code-docstring pairs for 7 epochs, giving it a solid understanding of code semantics.

To improve naturalness, I manually prepared 250 handwritten samples with their corresponding code snippets. These were used to fine-tune a version of XVCLM for 2 epochs, helping it write more naturally. Initially, XVCLM could only generate satisfactory descriptions about 30% of the time.

With some human help, XVCLM was able to create 700 descriptions, allowing for another round of fine-tuning. After this, I found that XVCLM could describe code much more clearly. I repeated this process until it produced 10,000 samples for Python, JavaScript, Go, PHP, Java, and Ruby.

Example Usage

from transformers import pipeline, set_seed

summarizer = pipeline('text2text-generation', model='Binarybardakshat/XVCLM-MIN-DECT') 
code = "print('hello world!')"

response = summarizer(code, max_length=100, num_beams=3)
print("Summarized code: " + response[0]['generated_text'])

Summarized code: The following code is greeting the world.

How XVCLM-MIN-DECT Generates Natural Language Descriptions from Code

Experiments and Findings

While building XVCLM, I had several questions about what would most affect performance. I define a high-performing model as one that generates coherent and sensible outputs that accurately describe the code’s scope. Prior work has shown that larger models often perform better, so I trained a variety of sizes.

My main questions included:

• How does model size affect the outputs?
• Does pre-training combined with fine-tuning perform better than just pre-training?
• How well does each model generalize if trained only on one programming language?
• Can I reduce the number of training epochs needed?

Areas for Improvement

During my experiments, I found some common shortcomings of XVCLM:

• Outputs are sometimes too brief. When faced with longer inputs, XVCLM often summarizes in 7 words or less, missing important details.
• Descriptions can be repetitive, especially when the input has mixed contexts.
• Training XVCLM is computationally expensive; pre-training for 5 to 7 epochs takes a full day on an Nvidia A100 GPU.
• XVCLM struggles to describe poorly written code effectively.
• The model currently accepts only 512 tokens as input, which limits its ability to summarize longer programs.

What’s Next for XVCLM?

I believe XVCLM could lead to significant changes in how software is developed. Many software developers enjoy writing code, but tasks like documentation, review, and bug fixing can be tedious. A system like XVCLM could help automate these less enjoyable tasks, allowing developers to focus more on coding.

In the near future, I see XVCLM being used for:

• Faster onboarding for software teams of any size.
• Automating internal practices like code review and documentation.
• Breaking language barriers for multilingual teams.
• Helping students understand code more quickly.
• Clarifying functionality in open-source projects.

However, my vision for XVCLM is broader than just explaining code. I want to expand its capabilities to:

• Write full-length documents, such as README files or reports about code functionality.
• Answer questions about code.
• Create different levels of explanation for different audiences (e.g., for experienced developers versus high school students).
• Generate examples of how code could be used in practice.

These possibilities illustrate the wide potential of XVCLM as a general code description tool.

Release Philosophy

Ultimately, my goal is to make powerful machine learning systems accessible to as many individuals, groups, and organizations as possible. Since developing systems like XVCLM can be costly, I am releasing it as a paid product. Access to XVCLM (and other versions) will be available through:

• A fully managed, no-code product
• Developer API
• Product licensing
• Partnerships

These avenues will help fund the next generation of my models. In the future, I hope to make various versions of XVCLM available as open-source, as I believe in providing access to this technology for everyone. However, I cannot guarantee specific timelines.

My Linkedin-: (4) Akshat (binarybardakshat) Shukla | LinkedIn

Link For XVCLm-:Binarybardakshat/XVCLM-MIN-DECT · Hugging Face