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Marc Berman
Stevie Cline will tell you that writing code is the part of software engineering that gets all the attention, the part celebrated in hackathons, featured in portfolio projects, and discussed in technical interviews with an almost theatrical enthusiasm. Debugging, by contrast, is quieter, less glamorous, and considerably more revealing.
It is where a developer’s true understanding of a system gets tested, where assumptions collapse under the weight of reality, and where the engineers who will eventually become exceptional begin to separate themselves from those who will remain perpetually frustrated. For new software engineers entering the field, building a deliberate and structured approach to debugging is one of the highest-return investments they can make.
When something breaks, most junior developers instinctively start changing things, tweaking variables, commenting out blocks, hoping some combination of edits accidentally produces a working result. That approach occasionally succeeds, but it builds nothing.
Effective debugging demands a cognitive shift: treating every bug as a question with a precise, discoverable answer rather than a random misfortune. Programs behave exactly as instructed, so when output diverges from expectation, a misunderstanding is almost always the cause.
Cline learned this through her own project work, building everything from a personal budgeting tool to a 2D Python game, each introducing its own category of unexpected behavior.
“The biggest shift for me was realizing that bugs are information,” she says. “They’re the program telling you something true about how the code is actually behaving, even if it’s not how you intended it to behave. Once I started reading them that way, debugging stopped feeling like a crisis and started feeling like a conversation.”
One of the costliest mistakes new engineers make is attempting to fix a bug before reliably reproducing it. Without a consistent reproduction case, there is no way to confirm a fix actually worked or to understand what caused the failure.
The first objective in any debugging session is identifying the smallest set of inputs that triggers the problem. Stevie Cline treats reproduction as a non-negotiable first step, especially for developers feeling pressure to resolve issues quickly.
“There’s a temptation to jump straight to solutions, especially when you’re early in your career and you feel like the clock is running, but if you can’t reproduce the bug consistently, you’re essentially guessing. And guessing is slower than investigating, even when it doesn’t feel that way at the moment,” says Cline.
Reliable reproduction also has the practical benefit of making it possible to verify the fix before shipping, a step that is easy to skip when confidence outruns certainty.
Modern development environments offer debugging tools of remarkable sophistication, and new engineers who rely exclusively on print statements are leaving significant capability on the table.
Integrated debuggers available in environments like Visual Studio Code, IntelliJ IDEA, and PyCharm allow developers to pause program execution at any point, inspect the current state of every variable, step through code line by line, and observe exactly how data transforms as it moves through a function or across a call stack. Mastery of these tools, setting breakpoints, using watch expressions, stepping over versus stepping into function calls, accelerates debugging dramatically.
Structured logging remains indispensable where interactive debuggers are impractical, such as deployed server-side applications or asynchronous processes. Thoughtful logging captures not just that something happened, but what state the system was in when it did.
Developers who log with intention create systems that communicate their own behavior. Cline applies both strategies deliberately, reaching for the interactive debugger on local logic errors and relying on structured log output when issues surface in data pipelines or event-driven systems.
Stack traces intimidate many junior developers, dense blocks of technical output that seem, at first glance, designed more to obscure than to illuminate. In reality, a well-formed stack trace is one of the most informative artifacts a broken program can produce. It records exactly which function calls were active when the failure occurred, in what order they were invoked, and at which line the exception was raised.
Reading a stack trace from the bottom up, identifying the originating call, and tracing the sequence of function invocations that led to the error transforms an overwhelming wall of text into a precise map of what went wrong. Error messages deserve the same careful reading.
The tendency to copy an error message directly into a search engine without first attempting to understand it is understandable, and sometimes appropriate, but developers who skip the comprehension step miss opportunities to deepen their understanding of the tools and languages they work with. Parsing what an error message is actually communicating, even when it is unfamiliar, builds pattern recognition that pays dividends across an entire career.
“I used to see a stack trace and immediately feel overwhelmed,” Cline admits. “But they’re actually incredibly useful once you know how to read them. Most of the time, the answer is right there, you just have to know where to look and what you’re looking at.”
The debugging strategies that serve a developer well on a solo project translate directly into professional team environments, where the ability to diagnose and resolve issues quickly, and communicate findings clearly, is one of the most visible forms of competence a new engineer can demonstrate.
Documenting what was found, what was tried, and what ultimately resolved an issue creates institutional knowledge and builds credibility with senior colleagues who can see the quality of thinking behind the conclusion.
For Stevie Cline, debugging is real development work, pursued with the same rigor and intentionality as writing new features. New software engineers who internalize that perspective early will find that their growth accelerates in ways that surpass any single technical skill.
Stevie Cline recently graduated from the University of Maryland with a B.S. in Computer Science and a passion for turning ideas into software solutions. She has built projects ranging from a personal budgeting app to a 2D Python game, and she volunteers as a tech tutor to help others find their footing in programming.
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