The Self-Teaching Reality: How Developers Actually Learn

The curriculum was invisible, but you learned it anyway.

The Invisible Curriculum

There's a peculiar gap in software engineering that we rarely discuss openly: the vast distance between what universities teach and what professional programmers need to know. I don't mean the usual complaints about outdated technologies or the lack of real-world frameworks in the curriculum. I mean something more fundamental - a structural mismatch between academic exercise and professional practice that forces nearly every programmer to become self-taught, regardless of their formal education.

Consider what makes a university programming assignment viable as an assignment. It must be:

• Completable in a fixed timeframe (usually days or weeks)
• Gradeable by someone who didn't write it (the instructor)
• Demonstrably correct through test cases or visible output
• Standalone enough to not require integration with existing systems

Now consider what makes professional software viable as software:

• Maintainable over years, often by people who didn't write the original code
• Usable by people with different mental models and assumptions than the creator
• Testable in ways that reveal not just correctness, but reliability under stress, edge cases, and misuse
• Integrable with systems built on different assumptions, often by different organisations

These two lists are nearly orthogonal. The constraints that make an academic project gradeable actively work against the constraints that make professional software functional.

The Consequence: Trial and Error as Methodology

When fresh graduates encounter their first professional codebase, something predictable happens. They've spent years learning algorithms, data structures, and language syntax. They've built small projects that worked. They understand Big O notation and can implement a red-black tree. But none of this tells them how to add a feature to a 500,000-line codebase where changing one function might break something three layers away that depends on its side effects.

The implicit message they receive is: "You're smart, so you figure it out."

This is an acknowledgment of a truth. There is no transferable body of knowledge that answers the question: "How do I work with this codebase?" The best your colleagues can offer is: "Here's how I learned to work with this codebase," which is inevitably a story of trial and error.

So trial and error becomes the primary methodology. Not because it's effective, but because it's the only methodology available.

You make a change. You run it. It breaks something unexpected. You investigate why. You learn one specific fact about this codebase that Module A depends on the timing behavior of Module B, or that this function gets called recursively even though it looks iterative, or that this configuration object is actually shared mutable state. You accumulate these facts, one debugger session at a time.

This is phenomenally expensive. Every piece of knowledge is paid for with time and frustration. And worse, the knowledge isn't portable. The next codebase you work on will have different implicit assumptions, different hidden dependencies, different gotchas.

The Second Strategy: Imitation

Since trial and error is so costly, programmers quickly develop a second strategy: find code that looks similar to what you're trying to do, and imitate its structure.

This seems reasonable. It's how we learn many skills, watch someone who knows what they're doing, copy their approach, gradually understand why it works. But in software, imitation has a fatal flaw: the code you're imitating was probably written by someone employing the same strategy.

You're imitating someone's imitation of someone else's trial-and-error solution to a problem that might not even be the same as yours. The original context is lost. The reasons for specific choices are lost. What remains is a pattern, a shape that works without clear understanding of why or under what conditions.

This compounds over time. Codebases accumulate layers of imitated patterns, each generation slightly more removed from the original reasoning. When someone finally asks Why do we do it this way? the answer is usually "Because the existing code does it this way." And when you trace it back far enough, you either hit a programmer who left the company years ago, or you discover it was itself copied from somewhere else.

The tragedy is that sometimes the imitated code was correct - but correct for constraints that no longer apply. The original programmer was working around a limitation in a library version from 2015. That limitation was fixed in 2017. But the workaround lives on, cargoculted through a dozen files, because no one understands it well enough to question whether it's still necessary.

Why Universities Can't Solve This

You might wonder: why don't universities teach maintainability, testability, and long-term thinking about code? Why don't they require students to work on multi-year projects, to maintain each other's code, to deal with changing requirements?

The answer is structural, not philosophical. Universities operate in semesters. Students take multiple classes simultaneously. Professors have research obligations. The entire system is optimised for discrete, evaluable units of learning delivered in fixed time windows.

To genuinely teach long-term software thinking, you'd need:

• Projects that span multiple semesters (but students' schedules don't align)
• Code that outlives individual students' tenure (but who maintains it between cohorts?)
• Realistic pressure to keep systems running (but educational software has no real users)
• Consequences for code that's hard to modify (but each assignment is a fresh start)

Some universities try. They run software engineering courses where teams build larger projects. But even these run for one semester, maybe two. The fundamental constraint remains: you can't teach maintenance without something to maintain, and you can't teach long-term thinking in a short-term institution.

The result is that universities do what they can do well: teach fundamentals. Data structures, algorithms, theory, language mechanics. These are genuine valuable - a programmer without this foundation will struggle. But they're not sufficient. They're the vocabulary of programming without the grammar of professional practice.

The Gap Between Graduation and Wisdom

Every programmer who's been in the field for a decade has accumulated a certain kind of knowledge. Let's call it wisdom to distinguish it from mere information. This wisdom includes things like:

• When to refactor and when to leave working code alone
• How to read unfamiliar code efficiently
• Which abstractions tend to last and which tend to break
• How to structure code so changes are localised
• When clever code is worth it and when it's a liability
• How to balance completeness with shipping
• Which technical debts compound and which don't matter

None of this is taught in universities. Not because universities don't want to teach it, but because it's unteachable in the abstract. It emerges from repeated encounters with the consequences of your decisions. You learn to avoid premature abstraction by creating premature abstractions and then suffering through the contortions required to make them fit new requirements. You learn to value readable code by debugging your own clever code six months after you wrote it.

The challenge is that this learning process takes years. And during those years, you're expensive. You make mistakes that senior developers have to fix. You make architectural decisions that seem reasonable but don't scale. You write code that works but is hard to maintain. This is simply the cost of training, and every company pays it.

But here's what's odd: we act as though this is inevitable. We say it takes experience as though experience is something that simply accrues with time, like sediment. We don't have a systematic way to transfer this wisdom from experienced programmers to new ones. We rely on proximity, put junior developers near senior ones and hope something rubs off.

The Self-Teaching Imperative

The uncomfortable truth is that essentially all programmers are self-taught, regardless of formal education. Not because they learned to code outside of school (though many did), but because the knowledge that makes you effective professionally is knowledge you must construct yourself through direct experience with real codebases, real constraints, and real consequences.

Your CS degree taught you that arrays have O(1) random access and linked lists require O(n) traversal. But it didn't teach you that in this codebase, the Config struct gets modified by threads in three different services, and if you don't acquire the lock in the right order you'll create a deadlock that only manifests under production load.

You learned that yourself. Through trial and error. Through imitating the locking patterns you saw elsewhere. Through debugging a production incident at 2 AM and finally understanding why that particular mutex exists.

This is the self-teaching reality: we have successfully industrialised software production while leaving the transmission of expertise almost entirely informal. We've created institutions - universities, bootcamps, online courses that teach programming as a subject, but we haven't created institutions that teach programming as a practice.

The question isn't whether this is a problem. Clearly it is. It makes programming unnecessarily expensive to learn, creates massive variation in quality between developers with similar years of experience, and means that most companies are simultaneously teaching and forgetting the same lessons as people join and leave.

The question is: what would it look like to do better? What would programming education look like if it acknowledged that the real learning happens through struggle with real code, and tried to make that struggle more systematic and less random?

If you want to see how such an education platform looks like visit Stackshala. We teach you through a methodology that addresses the issue with conventional software education.