Software Engineering Principles

6+ years in software engineering, and I still fall for this ONE coding mistake. Every. Single. Time. ⏰ I overcomplicate things. Early in my career, I thought: ✔ More code = better solution ✔ Complex logic = impressive skills ✔ Clever hacks = smart engineering I was wrong. The best engineers I’ve worked with all follow one simple rule: Keep it simple. Here’s what I’ve learned the hard way: 1️⃣ Readable code > Clever code If your code needs an explanation every time someone reads it, you’ve already lost. 2️⃣ Premature optimization is a trap Make it work first, then make it fast. Not the other way around. 3️⃣ The simplest solution is often the best If your solution feels unnecessarily complex, take a step back - it probably is. 4️⃣ Future-you will thank present-you for writing clean code Your code isn’t just for you. It’s for your team, your future self, and anyone who maintains it. 6+ years in, and I still remind myself daily: Write less, do more. 👇 What’s a coding mistake you still fall for? ♻️ Find it insightful? Consider reposting for your network!

What are the SOLID principles? 🤔 As a .NET developer, I'm always looking for ways to improve the quality and maintainability of my code. I want to share my experiences using the SOLID principles and how they've helped me create better code. First, let's define what we mean by SOLID. The acronym 𝗦𝗢𝗟𝗜𝗗 stands for: - Single responsibility principle - Open/closed principle - Liskov substitution principle - Interface segregation principle - Dependency inversion principle Let's break them down. 🔬 1. 𝗦𝗶𝗻𝗴𝗹𝗲 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗦𝗥𝗣) A class should have only one reason to change. In other words, a class should have a single, well-defined responsibility. That class should entirely encapsulate responsibility. 2. 𝗢𝗽𝗲𝗻/𝗖𝗹𝗼𝘀𝗲𝗱 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗢𝗖𝗣) Software entities (classes, functions, etc.) should be open for extension but closed for modification. You should be able to add new functionality to a class without changing its existing code, but you should not need to modify the class itself to do so. 3. 𝗟𝗶𝘀𝗸𝗼𝘃 𝗦𝘂𝗯𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗟𝗦𝗣) Subtypes must be substitutable for their base types. In other words, if a class is derived from another class, you should be able to use the derived class in the same way as the base class without any issues. 4. 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗦𝗲𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗜𝗦𝗣) Clients should not depend on interfaces they do not use. This means you should design your interfaces as specific and focused as possible. 5. 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 (𝗗𝗜𝗣) High-level modules should not depend on low-level modules. Both should depend on abstractions. Design your software so high-level modules depend on abstractions rather than concrete implementations. I struggled with creating code that was easy to understand and maintain, but the SOLID principles provided a clear set of guidelines to follow when designing software. One of the first principles I focused on was SRP. It encourages developers to design classes with a single, well-defined responsibility and to keep the code for that responsibility entirely encapsulated within the class. This makes it easier to understand a class's purpose and change its behavior without affecting other parts of the system. Imagine a class doing a few things, like performing calculations and interacting with the database. We could refactor the class to have a single responsibility of calculating the result and create separate classes for interacting with the database. This would make the code easier to understand and maintain. Hope that was helpful. :) P.S. If you enjoyed this post, repost it ♻️ to share it with your audience.

I have seen more systems struggling because of wrong code than slower ones. The fact remains, most engineers optimise too early. About 8 years ago, my principal engineer once told me: Performance is almost always the last thing you should be thinking about. As an SDE-2, this did not make sense :) After a few follow-ups, I understood why he meant that. The order that actually matters is this. First, is the code correct? Does it do what it is supposed to do? Second, can someone maintain it six months from now without wanting to quit? Third, is it fast to read and write? Only after all three does performance even enter the conversation. The reason this order exists is simple. A fast, unmaintainable codebase is a liability. A performant-but-wrong system is worse than a slow, correct one. You cannot optimise your way out of a bug. Now, this is not universally true. Databases, high-frequency trading systems, and real-time embedded software are domains where performance is a first-class concern from day one. But those are the exceptions, not the default assumption you should bring to every PR. What is certainly true is that for most codebases, premature optimisation adds complexity, reduces readability, and solves a problem that does not exist yet. So, write correct code first. Then clean it. Then, only if the profiler gives you a reason, make it fast.

The 10 Rules NASA Swears By to Write Bulletproof Code: 0. Restrict to simple control flow ↳ No goto, setjmp, longjmp, or recursion. Keep it linear and predictable. This ensures your code is easily verifiable and avoids infinite loops or unpredictable behavior. 1. Fixed loop bounds ↳ Every loop must have a statically provable upper bound. No infinite loops unless explicitly required (e.g., schedulers). This prevents runaway code and ensures bounded execution. 2. No dynamic memory allocation after initilization ↳ Say goodbye to malloc and free. Use pre-allocated memory only. This eliminates memory leaks, fragmentation, and unpredictable behavior. 3. Keep functions short ↳ No function should exceed 60 lines. Each function should be a single, logical unit that’s easy to understand and verify. 4. Assertion density: 2 per function ↳ Use assertions to catch anomalous conditions. They must be side-effect-free and trigger explicit recovery actions. This is your safety net for unexpected errors. 5. Declare data at the smallest scope ↳ Minimize variable scope to prevent misuse and simplify debugging. This enforces data hiding and reduces the risk of corruption. 6. Check all function returns and parameters ↳ Never ignore return values or skip parameter validation. This ensures error propagation and prevents silent failures. 7. Limit the preprocessor ↳ Use the preprocessor only for includes and simple macros. Avoid token pasting, recursion, and excessive conditional compilation. Keep your code clear and analyzable. 8. Restrict pointer use ↳ No more than one level of dereferencing. No function pointers. This reduces complexity and makes your code easier to analyze. 9. Compile with all warnings enabled ↳ Your code must be compiled with zero warnings in the most pedantic settings. Use static analyzers daily to catch issues early. Some of these rules can be seen as hard to follow, but you can't allow room for error when lives are at stake. Which ones are you still applying? #softwareengineering #systemdesign ~~~ 👉🏻 Join 46,001+ software engineers getting curated system design deep dives, trends, and tools (it's free): ➔ https://lnkd.in/dCuS8YAt ~~~ If you found this valuable: 👨🏼💻 Follow Alexandre Zajac 🔖 Bookmark this post for later ♻️ Repost to help someone in your network

Clean code isn't just about readability —it's about creating maintainable, scalable solutions that stand the test of time. When we prioritize readability, simplicity, and thoughtful architecture, we're not just making our lives easier; we're creating value for our teams and organizations. A few principles that have made the most significant difference in my work over years: • Meaningful naming that reveals intent • Functions that do one thing exceptionally well • Tests that serve as documentation and safety nets • Consistent formatting that reduces cognitive load The greatest insight I've gained is that clean code is fundamentally an act of communication—with future developers, our teammates, and even our future selves. The time invested upfront pays dividends during maintenance, debugging, and onboarding. What clean code practices have transformed your development experience? I'd love to hear about the principles that guide your work. Image Credit - Keivan Damirchi

Software engineers often underestimate how a single line of code can impact the company's profits. And it could be a trivial log line to print information for debugging. 😫 😫 Few years ago, my team was owning an AWS Lambda that worked very well and required minimal intervention. One day my Manager asked me why is the CloudWatch cost $15,000 but Lambda's cost was $1,200 only. 😱 😱 I decided to root cause this issue and finally figured out the main culprit was redundant log lines in the lambda. Eliminating the log lines bought down the costs by 10x. 🚀 🚀 What was the main issue for high CloudWatch costs ? 👉 CloudWatch charges $0.5/GB for ingestion and $0.03/GB for storage 👉 Our AWS Lambda was logging close to 5MB data per second. 👉 It was logging the request and a huge response payload (~100KB) 👉 As a result, the overall log ingestion cost was high. How did we debug the issue ? We used the CloudWatch log metrics to check the data usage. And identified the log group that was resulting in increased bill amount. CloudWatch console tool helped in debugging the root cause. How can we prevent such issue in the future ? ✅ Only log useful information i.e exceptions, critical errors, etc. Avoid logging everything. ✅ Use log levels such as Debug, Warn, Info, Error, etc. ✅  Add filtering to filter only the Error/Warn logs before ingesting into CloudWatch ✅  Review the code carefully and assess the impact of log line on the costs. Treat debug lines like a vulnerability. ✅  Continuously monitor the CloudWatch costs and set alarms to warn the team of any high costs. One of the key takeaways from this story is that engineers must know what impact each line of code will have on the overall business. And accordingly adopt best practices to prevent high costs. In case you have experienced a similar issue in the past, you can post in the below comments what best practices you are following. 👇 👇 #tech #aws #cloud #cloudcomputing

Everyone talks about scale. But what kind of scale are you really designing for? When we were building Sprinto, scalability did matter, but not in the way you'd think. Most early-stage founders worry about computational scale: Can the system handle a million users? Will the infra hold up? But here’s what kept Raghuveer Kancherla and I up at night: Can 50 developers work on this codebase simultaneously without stepping on each other’s toes? Having run a software company before, I knew the bottlenecks don’t always come from users, they come from your own team as you grow. One day you have 3 engineers and the next, you’re onboarding your 20th hire. If you haven’t designed for collaboration, modular code, separation of concerns, test infra, deployment scaffolding, you end up in gridlock. Code reviews slow down, testing becomes painful, debugging takes longer than building, and then velocity stalls. So from Day 1, we asked ourselves: What makes this product collaboration-scalable, not just user-scalable? That’s where our energy went, because the moment you start thinking this way, you stop writing clever code and start writing clear code. And that is what scales.

The cheapest place to fix a mistake in a software project is on a piece of paper, not in six months of code. Writing a clear requirement is a great start. But the real skill, the thing that separates a good project from a great one, is actively trying to break the logic before you build it. Here at Full Metal, we call this pre-emptive debugging. We map out the "happy path," where the user does everything perfectly. But then we spend more time on the "unhappy paths." We ask a series of 'what if' questions. For a simple password reset feature, we'll ask: ❌ What if the user enters an email that isn't registered? ❌ What if they click the reset link after it has expired? ❌ What if they try to reuse an old password? Each of those 'what ifs' becomes a new requirement, closing a loophole that could have caused problems down the line. It's about finding flaws where they're free to fix. This also helps us avoid common pitfalls I've seen time and again. The biggest is the ambiguity trap: using fuzzy words like "fast" or "easy." My "fast" is not your "fast." Instead of "The system should be quick," we define it: "The system shall return a response within 500ms." One is a wish; the other is a testable fact. This meticulous approach might seem like a lot of work up front, but it saves a fortune in rework and frustration later on. We explore these common pitfalls and how to avoid them in our latest blog for SME leaders. Find the blog here: https://lnkd.in/eptHVTKA Have you ever had a project go a bit pear-shaped because of a single, unasked 'what if' question? #SoftwareEngineering #RiskManagement #DigitalTransformation

In programming, the concept of "Separation of Concerns" (SoC) involves modularization your code so different functionality is compartmentalized in to different functions and/or files. For example, you may put code to render sprites in a game in one file, code to update calculations of where the sprites should be in another file, and use a third file to drive a clock that schedules the calculation updates and rendering. This makes it easier for others (that includes yourself in three months) to understand your code, enable reuse of code components, to organize your test plans, and to debug the program. Provided you name the functions, files, and the folders they are in properly. When prototyping, I tend to code in a stream-of-consciousness manner, and end up with what I call a "Attention Deficit Hodgepodging of Concerns" (ADHoC). While prototyping I'm working out the architecture as I code, and thinking linearly. Jumping between files when I switch between concepts is too overloading, and causes me to lose track of the ideas. And at that point, the ideas are the most important thing. But then afterwards I have to fix the resulting mess, which involves reviewing the hodgepodge code to document the final architecture, and then cut & pasting appropriately into new functions and files. If you're a developer, do you have a similar experience?

Why Debugging Makes You a Better Engineer Writing features feels rewarding but debugging is where real engineering growth happens Because debugging forces you to: • Understand systems deeply • Read unfamiliar code • Think in edge cases • Stay patient under pressure In production, problems rarely come with clear answers Sometimes: • Logs are incomplete • Errors are misleading • The issue is happening somewhere completely unexpected And that’s where debugging changes your mindset Over time, debugging improves something more important than coding speed: engineering intuition You begin noticing patterns faster You ask better questions You think more systematically A lot of great engineers aren’t just good at building things they’re exceptionally good at figuring out why things broke in the first place