AI for Hypothesis Testing

Sometimes you stare at buggy code and have no idea what's wrong. This is where AI coding assistants become valuable debugging partners. They can suggest hypotheses you might not have considered, helping you break through when you're stuck.

How AI Helps With Debugging

AI assistants are good at pattern recognition. They've seen millions of code examples and common bugs. When you describe your problem, they can suggest:

Common causes for that type of error
Things to check based on your symptoms
Similar problems and their solutions
Questions to help you think through the issue

Think of AI as a knowledgeable colleague you can bounce ideas off — one who's seen a lot of bugs.

Describing Bugs Effectively

The better you describe the problem, the better AI can help. Include:

What you expected: "This function should return the average of a list of numbers."

What actually happens: "It returns a number that's too small."

The code: Share the relevant function or section.

Any error messages: Copy the full error if there is one.

What you've tried: "I checked that the list isn't empty."

My function calculates averages but returns wrong values.
For [10, 20, 30], it returns 20 instead of 20. Wait, that's correct.
For [10, 20], it returns 15 instead of 15. Also correct.
For [5], it returns 5. Correct.
For [], it crashes with ZeroDivisionError.

def average(numbers):
    return sum(numbers) / len(numbers)

Getting Hypotheses

Ask AI open-ended questions to generate ideas:

"What could cause this function to return wrong values for negative numbers?"
"Why might this loop run forever?"
"What would make this list be empty at this point?"
"I'm getting a KeyError here — what are possible causes?"

AI might respond with several possibilities: "The list could be empty because: (1) the input was empty, (2) a filter removed all items, (3) an earlier error prevented items from being added..."

Testing Suggestions Systematically

Don't just accept AI's first suggestion. Treat each idea as a hypothesis to test:

AI suggests: "The variable might not be initialized"
You test: Add a print statement to check the variable's value
Result: Variable is initialized correctly
Move to next hypothesis

Keep track of what you've tested. This prevents going in circles and builds understanding of your code.

AI's Limitations

AI can suggest possibilities, but it can't run your code or see your actual data. It might suggest causes that don't apply to your situation. You're still the detective — AI just helps generate leads to investigate.

Also, AI sometimes confidently suggests wrong things. Always verify suggestions against your actual code and behavior.

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