Do You Still Remember the Principles of Software Engineering?
We’re living through a curious phase of technology. It’s never been so easy to generate code, create tests, ask for architecture suggestions, or fix bugs with AI help. In a few seconds, a tool answers what before would take hours of research, trial, and error.
This seems great, and in many cases it really is. The problem starts when speed comes to be confused with engineering.
Many people today present themselves as software developers or software engineers, but faced with so much automation it’s worth revisiting a simple question: do you still remember the principles of software engineering?
In the end, AI accelerates execution. But foundation continues to be what sustains software when the rush passes.
A Brief History of the Beginning
In the beginning of computing, programming was much more about making it work than about making it last. Systems were smaller, teams were leaner, and complexity hadn’t reached the level we see today.
Over time, software stopped being just support and became central to banks, hospitals, companies, governments, transportation, and practically all modern life. And it was at that moment that an uncomfortable truth became evident: writing code wasn’t enough.
It was necessary to build systems that could be understood, tested, maintained, scaled, and trusted. That’s where software engineering gained strength as a discipline. Not just as the ability to program, but as the responsibility to build software consistently, safely, and sustainably.
Today we’re at another turning point. AI delivers speed, productivity, and a constant feeling of gain. Except it can also push us toward an old error in new clothing: producing a lot and thinking little.
The Principles of Software Engineering
1. Clarity
Good code isn’t the most sophisticated. It’s what can be understood by other people without suffering.
Example: a function called calc(x, y, z) says very little. While calculateDiscount(price, percentage, coupon) communicates intention with clarity.
When code is clear, maintenance becomes technical work. When it’s not, it becomes guessing.
2. Simplicity
Not every problem needs an elaborate solution. In engineering, simplicity isn’t technical poverty. It’s maturity.
Example: if a business rule can be solved with a direct and readable condition, it doesn’t make sense to create multiple abstractions just to look more architected.
Simple solutions tend to be easier to fix, evolve, and explain.
3. Separation of Responsibilities
Each part of the system should have a well-defined responsibility.
Example: an authentication module shouldn’t also send emails, generate reports, and control administrative permissions at the same time.
When everything does everything, any change spreads impact throughout the system.
4. Maintainability
All relevant software will be changed. Therefore, it needs to be easy to modify without breaking the rest.
Example: if adding a new payment method requires touching ten files without any standard, the system is hard to maintain.
The right question isn’t just “does it work today?” but also “can someone change this tomorrow?”.
5. Testability
If you can’t test with confidence, you also can’t evolve safely.
Example: a function that depends simultaneously on database, internet, system time, and global state is much harder to test than a function with predictable input and output.
Testability reduces fear of change.
6. Reuse Without Duplication
Reusing well is different from copy and paste.
Example: if the same CPF validation is spread across four different files, any adjustment becomes rework and risk of inconsistency.
When logic is centralized in the right place, the system becomes more reliable.
7. Reliability
Software needs to behave predictably, including when something goes wrong.
Example: a payment system can’t just fail in silence. It needs to record the error, inform the user, and allow safe recovery.
Reliability isn’t absence of failures. It’s the ability to handle them responsibly.
8. Scalability
Something that works for ten users can collapse with ten thousand.
Example: loading all data from a table into memory might seem acceptable at first, but becomes a bottleneck when the database grows.
Scalability is thinking about growth before it becomes a crisis.
9. Technical Responsibility
Not everything that works is right. Engineering demands responsibility with security, impact, and quality.
Example: storing passwords in plain text might even seem functional during a quick test, but remains technically unacceptable.
Those who build software also answer for the consequences of what they deliver.
Where Does AI Fit in All This?
AI is excellent for accelerating tasks. It can suggest code, identify patterns, fix errors, summarize documentation, and help with direction. The problem isn’t using AI. The problem is outsourcing thinking.
If someone accepts code without understanding, applies architecture without criteria, creates abstraction without necessity, and delivers software without validating implications, then that person is producing code, but not necessarily practicing engineering.
AI helps a lot. It just can’t occupy the place of technical responsibility.
So, Do You Consider Yourself a Software Engineer?
If you consider yourself a software engineer, you need to remember that your value isn’t just in writing code quickly. Your value is in making good decisions, building with clarity, maintaining simplicity, testing rigorously, and delivering solutions that continue to make sense after the initial excitement.
With AI’s help, perhaps you’re producing more. But perhaps you’re also forgetting what really matters.
And what really matters continues to be the same: think well, build right, and don’t abandon the principles that sustain software engineering.