Coding Agent: Automating Software Development with LLM

[Spoiler – I got ChatGPT to write this blog post based on an auto-generated Readme file summary of an auto-generated code repository. This is what the future of coding will look like. More nested generations than a Christopher Nolan movie.]

Software development is a complex and time-consuming process. With the advent of Artificial Intelligence, automating many parts of this process has become a possibility. Today, we’ll dive into the Coding Agent project, a cutting-edge tool that utilizes Language Learning Models (LLMs) to not only code but also manage GitHub repositories.

Introduction

The Coding Agent is an AI-driven tool that aims to build an agent capable of coding itself using an LLM. The agent is designed to return clear, concise Python code following a test-driven development strategy, with lines limited to 100 characters and full Python typing.

Abilities

The general abilities of the Coding Agent are as follows:

Code Management

• Code Generation: Using Python files for different purposes, the agent can write and refactor code, generate tests, and manipulate README files.
• Test Writing & Analysis: Through pytest, it not only writes tests but also analyzes test results, improving the code as needed.

GitHub Management

• Issue & Branch Management: Dedicated modules for handling GitHub issues and branches ensure seamless integration with GitHub projects.

Task & Prompt Management with LLM

• Interactive LLM Interface: The interaction with the LLM is handled through dedicated functions, enabling the generation of new code, tests, and task management.

Docstring & Summary Generation

• Automatic Documentation: The agent can generate docstrings for modules and functions, produce to-do lists, and summarize project code.

GitHub as a User Interface

The GitHub API is really easy to use via PyGitHub. This is cool as you can use GitHub as a data store and user interface for the Coding Agent.

For example, one of the functions uses the open issues as a todo list. The issues are reviewed for the easiest issue and this is selected as the next task to implement. Issues are then converted into task descriptions that are used to generate code.

Local git branches are generated for each set of autogenerated code so we can use existing version management schemes to track changes and always roll back if things go pear-shaped.

Agent Structure

The Coding Agent is organized into different modules and files, each having its own purpose:

• GitHub Management: Managing GitHub issues and branches.
• Code Management: Handling code reading, writing, and test generation.
• LLM Integration: Building prompts and managing tasks with the LLM.
• Agent Core: The main script coordinating all the components to perform tasks.

GitHub Integration

The project fully integrates with GitHub using personal access tokens (PAT). To connect to the repository data, it requires a series of steps that include creating an access token and adding it as a Codespace secret for the repository.

Testing: Ensuring Quality in Autogenerated Code

One of the fundamental aspects of the Coding Agent project is its commitment to maintaining code quality and integrity [Editor: err have you seen your own code ChatGPT?!]. This is achieved through rigorous testing, especially vital when dealing with autogenerated code.

Why Testing Matters

• Verification of Autogenerated Code: Autogenerated code can be highly complex, and ensuring that it meets the desired specifications is crucial. Testing offers a structured way to verify that the code works as intended.
• Adjustments and Refinements: When tests fail, it’s a signal that adjustments are needed. Users can modify the code, refine the logic, and iterate until the tests pass, ensuring the code aligns with the requirements.
• Continuous Cross-Check: As the project grows, new functions are added, and the codebase evolves, tests serve as a constant check against regression. They ensure that new developments don’t inadvertently break existing functionality.

How Testing Works in Coding Agent

• Using pytest: The project employs pytest, a robust testing framework for Python. It allows for the creation of simple yet powerful test cases to validate the autogenerated code.
• Test-Driven Development: By following a test-driven development strategy, tests are written before the code itself. This approach encourages clear thinking about the requirements and produces code that is more maintainable and error-resistant.
• Analyzing Test Results: Beyond just pass/fail, the project’s tools analyze test results to inform further code improvements. This analytical approach fosters a cycle of continuous refinement and enhancement.

In the world of software development, especially where code is autogenerated, testing is not just an optional extra; it’s a necessity. The Coding Agent project’s focus on testing assures that the code it generates is reliable, robust, and ready for real-world applications. It’s a testament to a modern, professional approach to software development where quality is embedded in every line of code.

Conclusion

The Coding Agent project is an innovative approach to automating the software development process. By leveraging the power of LLMs, it contributes to coding efficiency and quality, making it an essential tool for modern software developers.

For those interested in diving deeper, the entire project is available on GitHub. Feel free to explore the code, contribute, or even fork it to create your customized version.

Just be careful not to run code from LLMs directly without checking it first. I both check the code and run in a remote virtual machine (GitHub Codespaces).