Pseudo TDD with AI

Exploring Test-Driven Development with AI Agents

Over the past few months, I've been experimenting with a way to apply Test-Driven Development (TDD) by leveraging artificial intelligence agents. The goal has been to maintain the essence of the TDD process (test, code, refactor) while taking advantage of the speed and code generation capabilities that AI offers. I call this approach Pseudo TDD with AI.

How the Process Works

The AI agent follows a set of simple rules:

1. Write a test first.
2. Run the test and verify that it fails.
3. Write the production code.
4. Run the tests again to verify that everything passes.

I use the rules I defined in my base setup for augmented coding with AI. With these base rules, I can get both the Cursor agent and Claude Code to perform the TDD loop almost completely autonomously.

The refactoring part is not included automatically. Instead, I request it periodically as I observe how the design evolves. This manual control allows me to adjust the design without slowing down the overall pace of work.

Confidence Level and Limitations

The level of confidence I have in the code generated through this process is somewhat lower than that of TDD done manually by an experienced developer. There are several reasons for this:

• Sometimes the agent doesn't follow all the instructions exactly and skips a step.
• It occasionally generates fewer tests than I would consider necessary to ensure good confidence in the code.
• It tends to generalize too early, creating production code solutions that cover more cases than have actually been tested.

Despite these issues, the process is very efficient and the results are usually satisfactory. However, it still doesn't match the confidence level of fully human-driven TDD.

Supporting Tools

To compensate for these differences and increase confidence in the code, I rely on tools like Mutation Testing. This technique has proven very useful for detecting parts of the code that weren't adequately covered by tests, helping me strengthen the reliability of the process.

Alternative Approaches Explored

In the early phases of experimentation, I tried a different approach: directing the TDD process myself within the chat with the AI, step by step. It was a very controlled flow:

"Now I want a test for this."
"Now make it pass."
"Now refactor."

This method made the process practically equivalent to traditional human TDD, as I had complete control over every detail. However, it turned out to be slower and didn't really leverage the AI's capabilities. In practice, it worked more as occasional help than as an autonomous process.

Next Steps

From the current state of this Pseudo TDD with AI, I see two possible paths forward:

1. Adjust the rules and processes so the flow comes closer to human TDD while maintaining AI speed.
2. Keep the current approach while observing and measuring how closely it actually approximates a traditional TDD process.

In any case, I'll continue exploring and sharing any progress or learnings that emerge from this experiment. The goal is to keep searching for that balance point between efficiency and confidence that collaboration between humans and AI agents can offer.