Automate Your Python Code's Behavior with Bloom

Ever found yourself manually testing a function's edge cases, or writing a bunch of print() statements just to see if your code behaves as expected across different inputs? It's a common time sink. What if you could automate that entire process of evaluating your code's behavior, instantly?

That's the idea behind Bloom, a research project from the Safety Research team. It's a tool that automatically generates and runs tests to evaluate the behavior of your Python code. Think of it as a first-pass automated QA engineer that works directly on your functions.

What It Does

In simple terms, you give Bloom a Python function and a description of what it's supposed to do. Bloom then takes over, generating a variety of input arguments, running your function with those inputs, and checking if the outputs align with the described behavior. It automates the initial, repetitive stage of behavior verification, surfacing potential issues or unexpected outputs you might not have considered.

Why It's Cool

The clever part is how it works under the hood. Bloom leverages a large language model (LLM) to understand your function's purpose from your description. It then uses that understanding to do two key things:

1. Generate Diverse Test Inputs: It doesn't just test with 1, 2, 3. It tries to create a range of inputs—including edge cases—that are relevant to the function's domain.
2. Predict Expected Outputs: For each generated input, the LLM predicts what the correct output should be, based on the behavior description you provided.

By comparing your function's actual output to the LLM's predicted "correct" output, Bloom can flag discrepancies for you to review. This is particularly useful for:

• Catching overlooked edge cases in algorithmic or data processing functions.
• Quickly validating the core behavior of a function during development.
• Documentation testing, in a way—checking if your code actually does what you say it does.

How to Try It

The project is open source on GitHub. Since it's a research prototype, the best way to try it is to clone the repo and run it locally.

1. Clone the repository:

   git clone https://github.com/safety-research/bloom
   cd bloom
   

2. Follow the setup instructions in the README.md. You'll likely need to install dependencies (probably a simple pip install -r requirements.txt) and set up an API key for the LLM service it uses.
3. The repo should contain examples showing how to define your function and its behavioral description for Bloom to evaluate.

Head over to the Bloom GitHub repository to get started and see specific examples.

Final Thoughts

Bloom feels like a glimpse into a useful future development workflow. It's not a replacement for your carefully crafted unit test suite, but it's a powerful ally for the initial "does this even work?" phase. It's like having a rubber duck that can automatically generate test cases. For developers, it could mean spending less time on boilerplate test writing and more time on complex logic and architecture. It's definitely worth a look the next time you're building a new, tricky function and want a second opinion on its behavior.

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