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To explain debugging well, make each decision visible: state what failed, separate evidence from guesses, form a testable hypothesis, choose an observation that could prove it wrong, and check whether the fix changes the result. For example, if a test expects a total of 10 but the program returns 7, do not jump straight to a code change. Say what you observed, predict what a useful test or debugger inspection should show, then compare that prediction with what actually happens.
How do I explain my debugging process to a junior developer?
Think aloud in short, evidence-led steps. The aim is not to narrate every keystroke; it is to show how an observation changes what you think and what you do next. Keep these distinctions clear:
- Expected behavior: what the test, requirement, or user scenario says should happen.
- Observed behavior: what the program actually did.
- Hypothesis: a possible explanation, not yet a fact.
- Test: an input, execution, or inspection that could support or weaken the hypothesis.
Use an invented illustration: a function should add a delivery fee when an order is below a threshold, but a test returns a total that is too low. You might say:
- “The test expected 10, but the program produced 7.”
- “I think the mismatch may be in the fee condition because this order is below the threshold, yet the result looks like the fee was skipped.”
- “If that is right, an order just below the threshold should take the fee branch. An order at the threshold may behave differently; let’s check both rather than assume.”
- “Let’s run those cases or inspect the condition when the function reaches it, then compare the actual state with our prediction.”
- “That observation supports—or weakens—the idea. We’ll make the smallest relevant change and rerun the test.”
- “What does this variable represent, and what did the last observation tell us?”
This is a teaching script, not a protocol tested word-for-word. Its value is that the junior sees the gap between evidence and interpretation, and gets a chance to predict what will happen before the next action.
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How do you teach someone to debug code?
Start with the failure, not the patch
Ask the learner to describe the mismatch in concrete terms: which input was used, what result was expected, and what result appeared. If the failure is intermittent or depends on a sequence of actions, capture that sequence. A precise failure description narrows the question without pretending the cause is already known.
Ask for a prediction before each observation
Before running a test, adding a print statement, or stepping through code, ask: “What do you expect we’ll see if your explanation is right?” Also ask what result would make them reconsider. A useful debugging action distinguishes between plausible explanations; an observation that would look the same either way teaches little.
Choose inputs deliberately. If two explanations differ only at a boundary, test values on both sides of that boundary. If the suspected issue concerns an empty collection, a typical non-empty input may not reveal it. Research on novice tracing reports that learners can choose inputs that fail to expose behavior, trace incorrectly because they misunderstand syntax, or skip tracing when it would help. Explicitly discuss why a particular input is informative, including whether it could contradict the learner’s current idea (2023 SIGCSE study on tracing to explain code).
Explain what variables mean in this code
Do not stop at labels such as “counter” or “result.” Ask what a variable represents at this point in the program, how its value changes, and which code uses it. In a 2023 study with introductory programming students, prompting learners to explain variable purpose helped them focus on useful subsets of code. Merely identifying prominent code features or naming variable roles was rarely helpful on its own in that study (2023 study of beacons, variable roles, and tracing).
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After an observation, ask what it rules in or out. If the result does not match the prediction, treat that as useful information: revise the hypothesis or the mental model of the code. The point is not to reward a correct first guess; it is to make updating a guess in response to evidence normal.
Verify the correction against the original failure
Once the likely cause is supported, make a focused change and rerun the test that exposed the problem. Add or run a nearby case when it checks a distinct behavior, such as a boundary or empty input. Ask the junior to explain why the result now matches the requirement. A passing test is evidence for the correction in that case, not proof that every possible case is correct.
When should I use a debugger instead of print statements?
There is no universal winner. Think of ordinary execution and test inputs as a broad view of what the program does, and an interactive debugger as a way to inspect a smaller region, such as the values and control flow at a particular point. Choose according to the question the learner needs to answer, and switch tools when the question changes.
| Situation | Useful starting point | What to make visible |
|---|---|---|
| Code is familiar or relatively simple, and the question concerns overall behavior across inputs | Run the code or relevant tests | Which input produces the mismatch, and whether the result changes across cases |
| Code is unfamiliar, control flow is complex or nested, or the learner is confused about a small region | Use a debugger to step through the relevant path | Which branch ran, how key values changed, and where execution diverged from the prediction |
| You need both the broad behavior and a close look at one point | Switch between execution and debugger inspection | Whether the detailed state explains the result seen in the broader run |
An ACM ICER 2024 randomized study of 421 participants found that novices were more often successful at comprehending code when code execution was available, while debugger success improved as code complexity increased. In think-aloud interviews with 18 participants, novices tended to choose execution for simpler or familiar code and debuggers for complex or unfamiliar code, or when confused about a small part. Higher-performing novices switched between the broad view of execution and detailed debugger inspection. The results concern code comprehension in a study setting; they do not establish that either tool always improves production debugging outcomes (Hassan, Zeng, and Zilles, ACM ICER 2024).
When stepping through nested loops or other complex structures, pause to check the learner’s understanding of which iteration and branch are active. A debugger can expose state, but it cannot choose an informative input or explain what a value means on the learner’s behalf.
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How do I explain what I’m thinking while debugging?
Use compact statements that distinguish observation, interpretation, and next action. For example:
- “I know the output differs from the expected value; I do not know the cause yet.”
- “This branch is one possibility because the input is below the boundary.”
- “Let’s choose a case where that explanation predicts a different result from the alternative.”
- “The value at this point does—or does not—match our prediction, so I’m updating the hypothesis.”
Do not turn think-aloud into a running commentary on every line. Explain decisions that matter: why you chose this input, what result you expect, what the observation means, and why the next step follows. If you do not know, say so and identify an observation that would help.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I respond when a junior uses AI for debugging?
Treat AI-generated analysis as a hypothesis to inspect, not as evidence that the cause has been found. Ask what in the code or failure supports the explanation, what observation could challenge it, and whether the suggested change passes the relevant tests. Keep ownership of the reasoning with the learner: they should be able to explain what the proposed change does and why the observed result supports it.
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An ACM ICER 2024 study of a pedagogically designed chatbot found variation in learners’ help-seeking and engagement depending on their familiarity with suggested strategies. Interviewed students valued the chatbot’s content and experiential knowledge but did not regard it as their primary source for learning debugging strategies. Those findings concern novice learners and that study’s chatbot, not the workplace effectiveness of current AI coding products (ACM ICER 2024 study of chatbot help-seeking).
What should you avoid teaching as an absolute rule?
- “Never make a small edit.” A 2023 submission-log study reports that minor edits can be beneficial. Whether an action helps depends on what it tests and how the developer checks the result.
- “More attempts mean worse reasoning.” The same study found that measuring the width versus depth of the same debugging behavior could produce opposite associations with efficiency. Attempt counts alone do not show whether someone is reasoning well.
- “Always use the debugger” or “print statements are enough.” The tool-choice evidence supports strategic switching, not a single best tool for every codebase or question.
- “This script works for every team.” Most of the cited evidence concerns introductory learners, code-comprehension tasks, educational interventions, or course submission logs. It does not prove one mentoring approach works for every workplace, language, or experience level.
Keep the standard practical: does the action test a stated idea, and does the developer inspect the result? That is more informative than whether the action looks sophisticated or whether the first attempt succeeds. The 2023 submission-log study is available through the ACM Digital Library.
What the evidence does—and does not—show
Computing-education studies support making code behavior observable, choosing trace inputs that can distinguish explanations, explaining variable purpose, and switching between execution and debugger inspection as needed. They offer useful teaching ideas, not a proven universal workplace protocol. The findings are drawn mainly from novice learners and educational settings, so adapt the approach to the junior’s experience, the language, and the problem at hand.
The Debugging in Novice Programmers research group describes a review begun in fall 2005 and reports having examined more than 50 papers. That is a historical count from the group, not a current count of the literature or a population-level finding (Debugging in Novice Programmers research group).
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