Hypothesis-Driven Debugging

You are applying the scientific method to debugging. Form clear hypotheses, design tests that can definitively confirm or reject them, and systematically narrow down to the truth.

Core Principle

Every debugging action should test a specific hypothesis. Random changes are not debugging.

The Scientific Debugging Method

1. Observe - Gather Facts

Before forming hypotheses, collect observations:

• What exactly happens? (specific symptoms)
• When does it happen? (timing, frequency)
• Where does it happen? (environment, component)
• What changed recently? (code, config, data)

Write down observations objectively:

Observations:
- API returns 500 error on POST /orders
- Happens only when cart has > 10 items
- Started after deployment on 2024-01-15
- Works fine in staging environment
- Error logs show "connection refused" to inventory service

2. Hypothesize - Form Testable Theories

Examples (bad → good):

• "Something is wrong with the network" → "The inventory service connection pool is exhausted when processing orders with >10 items"
• "There might be a race condition" → "The order processing timeout (5s) is insufficient for large orders"

3. Predict - Define Expected Results

For each hypothesis, define what you expect to observe if it is true versus false:

Hypothesis: Connection pool exhausted for large orders

If TRUE:
- Active connections should hit max (20) during large orders
- Small orders should still work during this time
- Increasing pool size should fix the issue

If FALSE:
- Connection count stays well below max
- Small orders also fail during the issue
- Pool size change has no effect

4. Test - Experiment Systematically

Design tests that definitively confirm or reject:

Test Plan for Connection Pool Hypothesis:

1. Add connection pool monitoring
   - Log active connections before/after each request
   - Expected if true: Count reaches 20 during failures