Using large language models to generate program code

Using large language models to generate program code

Large Language Models (LLM) have had a significant impact on modern software development methods, especially in the field of automatic code generation. This article examines the state of research in the field of using LLM for code generation, analyzes the advantages and limitations of existing approaches, and suggests promising areas for further research.

Code generation has traditionally been considered a difficult task requiring a deep understanding of the subject area and algorithmic thinking. However, with the advent and development of large language models such as GPT (Generative Trained Transformer), Codex, and Gemini, automatic code generation has become possible at a whole new level of quality and scalability.

Basic approaches and technologies

1. Transformers and language models

Transformer architectures, originally developed for natural language processing tasks, form the basis of modern code generation solutions. GPT and Codex are trained on huge arrays of texts, including billions of lines of source code from public repositories GitHub, Stack Overflow and other sources.

2. Generation methods

Code generation using LLM is performed via:

• Continuation of the context (completion);

• Generation according to instructions (instruction following);

• Conversational interaction.

These approaches allow the model to predict the next piece of code, interpret and follow user instructions, and refine tasks through dialogue.

Advantages of using LLM for code generation

1. Increased productivity

Using LLM allows you to significantly speed up the development process by automating the writing of sample code, data structures, and algorithms.

2. Lowering the entry threshold

Developers with less programming experience get the opportunity to create high-quality and functional code.

3. Reducing errors

Automatic code generation reduces the likelihood of human error, especially in standard tasks and repetitive patterns.

Limitations of current approaches

Despite significant successes, there are also limitations in the use of LLM:

1. Lack of contextual understanding

Models can make mistakes if the task context is too complex or poorly formulated.

2. Vulnerability to unsafe code generation

LLMs may generate code that does not meet security and reliability requirements.

3. Dependence on training data

The quality of the generated code directly depends on the training data, which can lead to the transfer of errors and vulnerabilities from the source material.

The prospects

Promising areas for further research are:

• Improving contextual understanding of models;

• Integration of static analysis and other code quality checks;

• Creation of specialized models for specific programming languages and domain domains;

• Improve the security and reliability of automatically generated code.

Conclusion

The use of large language models to generate software code is a rapidly developing area of research that is already having a significant impact on software development. Despite the existing limitations, further improvement of LLM opens up great opportunities for automating programming, improving productivity and quality of development.