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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesExplain each debugging move as a test of an idea: state what failed, what you expected, what you suspect, and what observation could prove that suspicion wrong. For example, if a test expects a total of 10 but the program returns 8, do not jump straight to a code change. Say what you know, choose a revealing input, inspect the relevant execution, then rerun the test after a focused fix.
How do I explain my debugging process to a junior developer?
Make the reasoning visible without presenting guesses as facts. The junior should be able to follow the path from a reproducible failure to an observation and then to a verified change. A useful teaching sequence is:
- Describe the mismatch: “The test expected 10, but the program produced 8.” Identify the input and the actual result, not just that “it is broken.”
- Separate evidence from inference: “I observed that the total is two too low. I suspect one item is being skipped, but we have not confirmed that yet.”
- State a testable prediction: “If the loop is skipping the final item, this three-item input should leave the last item out of the total.” Include what result would weaken the idea.
- Choose an observation: Run that input, inspect a relevant variable, or step through the suspected region. Explain why that observation can distinguish the current idea from alternatives.
- Compare prediction with execution: Ask the junior what they expect to see before running it. Then inspect what actually happens and revise the hypothesis if needed.
- Make a focused correction and verify it: Change the relevant logic, rerun the failing case, and check a nearby case that could catch a regression.
This sequence is a practical synthesis of studies of code comprehension, tracing, and novice tool use—not a universally validated workplace protocol. The studies support making reasoning and observations explicit; they do not establish that every team or language should follow one fixed script.
An illustrative example
Imagine a function meant to add every number in a list, but a test with three numbers returns the sum of only the first two. The code and bug here are invented for illustration. You might narrate:
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“The test expected 12, but we got 7. The result matches the first two numbers, so I suspect the loop stops before processing the last one. If that is right, stepping through a three-number input should show the loop ending before the third value is added. What do you predict the running total will be at each step?”
“Let’s inspect the loop and compare the actual values with that prediction. The trace shows the loop condition becomes false before the final item. That supports the hypothesis. We’ll adjust the boundary condition, rerun this test, and then try an empty list and a one-item list to check the boundary cases.”
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The point is not to perform certainty. If the observed state differs from the prediction, say so and form a better hypothesis. A junior learns more from seeing a reasonable idea revised in light of evidence than from hearing a confident explanation invented after the fix.
How do you teach someone to debug code?
Ask the learner to participate in the reasoning rather than simply watch you operate the editor. Before running a test or stepping through code, invite them to predict an outcome. Afterward, ask what the observation tells them and what it does not tell them.
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- Ask what a variable represents. “What does
totalmean at this point?” is more useful than “What is this variable called?” A study of introductory learners found that explaining variable purpose helped them focus on useful parts of code; merely identifying code beacons or naming variable roles was rarely helpful by itself. - Use inputs that can distinguish explanations. If two hypotheses predict the same result for a typical input, that input will not help choose between them. Pick a case where their predictions differ, and include a case that could contradict the current hypothesis.
- Make tracing an explicit skill. Ask the learner to track relevant values and control flow step by step. A 2023 SIGCSE study identified failures to trace when it would help, incorrect traces caused by language misunderstandings, and inputs that failed to reveal behavior as obstacles to understanding code.
- Check understanding of the language construct. A surprising trace may reflect a misunderstanding of syntax or evaluation order rather than the bug you first suspected. Clarify the construct before treating the trace as evidence for a program-level explanation.
- Let the learner explain the next observation. Ask, “What would we expect to see if that is true?” and “What result would make us change our minds?” This keeps each action tied to a question.
Do not grade the reasoning by the number of edits or attempts alone. A 2023 submission-log study found that minor edits can be beneficial and that measuring the width versus depth of the same debugging behavior can produce opposite associations with efficiency. The more useful mentoring question is whether an action tests a stated idea and whether the result is checked.
When should I use a debugger instead of print statements?
There is no universal winner. Use the method that best answers the question at hand, and switch when the question changes. In an ACM ICER 2024 randomized study of 421 participants, novices were more often successful at code comprehension when code execution was available, while debugger success improved as code complexity increased. Think-aloud interviews with 18 participants found that novices tended to choose execution for simpler or familiar code and debuggers for more complex or unfamiliar code, or when confused about a small region. These are findings about novice code understanding, not a guarantee about production debugging outcomes.
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| Situation | Useful first approach | Why it can help |
|---|---|---|
| Simple or familiar code; question concerns behavior across several inputs | Run the code or focused tests | Execution gives a broad view of outcomes and makes it easy to compare cases. |
| Complex or unfamiliar code, especially nested control flow | Use an interactive debugger | Stepping through relevant branches and inspecting state can make a difficult execution easier to follow. |
| A small region is confusing | Set a breakpoint or inspect execution around that region | Detailed inspection can focus attention on the point where the learner’s prediction and the program diverge. |
| Several plausible explanations remain | Choose a test input or observation whose predicted outcomes differ | The tool matters less than whether the observation can discriminate between hypotheses; a debugger and test execution can complement each other. |
The ICER 2024 authors recommend teaching learners to recognize complexity, step carefully through structures such as nested loops, check their understanding, and switch tools strategically. For a junior, describe the choice aloud: “A test will tell us whether this happens for several inputs; a breakpoint will show which branch this input takes.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I explain what I’m thinking while debugging?
Use short statements that label evidence, hypotheses, predictions, and decisions. Avoid narrating every keystroke or implying that an untested suspicion is already a diagnosis. This wording is a teaching aid, not a script tested verbatim in the cited studies:
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- “The test expected __, but the program produced __.”
- “I observed __. One possible explanation is __, but we have not tested it.”
- “If that explanation is right, this input or line should produce __. If it does not, I’ll revise the idea.”
- “What do you predict we’ll see? Let’s run that case or inspect this point, then compare the actual state with our prediction.”
- “That observation supports/weakens the idea because __. What else could explain it?”
- “Now we’ll make the smallest relevant change and rerun the test. What additional case would give us confidence that we did not break nearby behavior?”
If a junior is using AI-generated analysis, handle it the same way you would handle another proposed explanation: ask what evidence supports it, what observation could challenge it, and whether the suggested change passes relevant tests. An ACM ICER 2024 study of a pedagogically designed chatbot found that learners’ help-seeking and engagement varied with their familiarity with suggested strategies; interviewees valued the chatbot’s content and experiential knowledge but did not regard it as a primary source for learning debugging strategies. That study does not establish how effective current workplace AI coding products are.
What can these studies tell us—and what can’t they?
The evidence here is primarily about introductory learners, educational tasks, code comprehension, and course submission logs. It supports teaching purposeful tracing, choosing informative inputs, explaining variable purpose, and switching between broad execution and detailed inspection. It does not prove that one mentoring method works for every workplace, language, or experience level, nor that code-comprehension results automatically predict debugging success in production.
The Debugging in Novice Programmers research group’s page describes a literature review begun in fall 2005 and reports that the group had reviewed more than 50 papers. That is a historical count reported by the group, not a current total for the field. The page also records the group’s caution about applying then-current research directly to educators’ questions; it should be read in its original historical context rather than as a verdict on all later work.
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