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Debugging hypothesis

As explained in Learn AI-Assisted Python Programming, Second Edition

A debugging hypothesis is a proposed explanation for a program defect that guides investigation and testing.

A debugging hypothesis is a proposed explanation for a defect. Use it to turn a surprising test failure into a focused investigation: predict what is wrong, inspect the relevant state, and test whether the explanation holds. section 8.5

Build hypotheses from the failing behavior:

  • Identify the edge case that exposes the failure. section 8.5
  • Name a specific operation or value that may be wrong. section 8.5
  • Set a breakpoint at that operation and inspect execution state. section 8.5
  • If no useful hypothesis emerges, step through the function from its first line. section 8.5

A failing classroom-row test

A function reports three consecutive students when the expected result is four. Two hypotheses follow: updating count skips the final student, or updating max_count misses the final run. A breakpoint reveals that the final run is never compared with max_count; moving that comparison into the student branch fixes the result. section 8.5

How Learn AI-Assisted Python Programming, Second Edition covers it

How deep it goes
Works through competing explanations, breakpoints, variable states, and a correction, while deferring hypothesis-free stepping as a fallback.
Examples
Uses a consecutive-student classroom function, a failing doctest, competing count hypotheses, and debugger pauses.
What you can do after
Diagnose defects by proposing explanations, placing breakpoints, inspecting state, and validating corrections against focused tests.
How it connects
Connects debugging hypotheses with test cases, edge cases, breakpoints, and debuggers.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Learn AI-Assisted Python Programming, Second Edition. The section links point to the parts of the book it draws on.