The Self-Sabotaging Habits That Make Maintaining Software a Struggle

In my previous article, “Over-Engineered and Unusable”, I explored how poor abstractions can make software harder to use. But complexity doesn’t just come from over-engineering — it also creeps in as projects age.

What starts as a clean, structured codebase often turns into a tangled mess. Poor design decisions, neglected developer experience (DX), and fear of change pile up, making even simple tasks painful. Worse, bad practices get reinforced in the name of “consistency.”

Why does this happen, and how can we prevent it? Let’s break down the key reasons software projects become harder to maintain — and how to keep them from spiraling into chaos.

Not Implementing What’s Necessary for Long-Term Maintenance

Every software project starts with good intentions, but long-term maintainability often gets sacrificed for quick wins. Database design, system architecture, and best practices are rushed without considering their future impact. As a result, what seems like an efficient decision today can turn into a maintenance nightmare down the line.

Overusing Language-Specific Features Instead of Proper Implementation

Every programming language offers powerful features — reflection, metaprogramming, operator overloading — but just because you can use them doesn’t mean you should. Over-relying on language tricks instead of designing a clean, maintainable system often leads to:

• Code that’s hard to read and debug. Future developers (or even you, six months later) struggle to understand the cleverness.
• Reduced portability. What works great in one language might make migration impossible in the future.
• Unnecessary complexity. A simple if-else might outperform an overly abstracted pattern-matching solution.

A good rule of thumb is to use language features where they improve clarity, not just to show off expertise.

Not Understanding the Impact of Decisions

Many design choices seem minor at first — until they snowball into serious constraints. A few common examples:

• Choosing the wrong database structure. Normalization might be ideal for consistency, but it can kill performance when not balanced correctly.
• Over-modularizing code. Splitting everything into separate services can lead to unnecessary complexity, latency, and maintenance overhead.
• Ignoring technical debt. Quick workarounds accumulate, and before you know it, refactoring becomes too costly to justify.

What’s the Fix?

• Think long-term. Before making a decision, ask: Will this scale? Will it be easy to maintain?
• Keep it simple. Don’t over-engineer solutions — prioritize clarity over cleverness.
• Document key decisions. Future developers (including yourself) will thank you.

The best code isn’t the most sophisticated — it’s the code that stands the test of time.

Not Thinking About Developer Experience (DX)

Software is built for users, but it’s developers who create and maintain it. Ignoring Developer Experience (DX) might seem harmless — after all, the end product is for users, not developers, right? Wrong. A poor DX slows down development, increases bugs, and makes long-term maintenance a nightmare.

Why Bad DX Slows Down Development?

• Poorly structured codebase: If developers struggle to understand the project, every task takes longer than it should.
• Lack of automation: Manual deployments, complicated setup processes, and inconsistent environments waste valuable time.
• Missing or outdated documentation: When developers have to dig through code just to understand basic functionality, mistakes happen.

A project with bad DX forces developers to work around the system instead of with it, leading to slow progress and unnecessary frustration. These workarounds just make this situation worse every time they are implemented.

Common Mistakes That Hurt DX

• Cryptic build processes: If setting up the project requires reading multiple outdated README files, turning the project setup into a wild goose chase… DX is suffering.
• Inconsistent coding practices: Without clear guidelines, doesn't every file feel like it belongs to a different project? Or is it just me?

How to Improve DX

• Maintain good documentation. Future developers (including you) should have a clear reference for how things work.
• Prioritize automation. Use CI/CD pipelines, automated tests, and scripts to streamline workflows.

Consistently Making Mistakes

Mistakes happen in every software project, but what really destroys the quality of development and the product is when teams keep repeating them — or worse, refuse to correct them. Instead of fixing issues properly, developers often follow the flawed implementation for the sake of “consistency.” This mindset leads to long-term technical debt and a codebase that becomes harder to maintain with every new feature.

Common Pitfalls That Keep Projects Stuck

• Copying bad patterns for consistency: If an initial implementation is wrong, sticking to it doesn’t make it right.
• Fear of refactoring: Developers avoid necessary changes, even when it’s obvious they would improve the system.

Why Teams Fall Into This Trap?

• “It works, so don’t touch it.” Just because something functions doesn’t mean it’s well-implemented.
• “We’ve always done it this way.” A process that made sense early on may no longer be the best choice as the project evolves.
• “Fixing it takes too much time.” But the longer bad code stays, the more expensive it becomes to fix.

Breaking the Cycle

• Encourage improvements, even if it means breaking old patterns. A codebase should evolve, not just accumulate workarounds.
• Refactor when necessary. Don’t be afraid to improve bad implementations, even if they’ve been around for a while.
• Adopt a culture of learning. Mistakes should be a lesson, not a precedent to follow.

Code Coverage Madness

Test coverage is important, but obsessing over a high percentage without considering test quality can do more harm than good. Many teams push for arbitrary coverage targets, leading to a flood of meaningless tests that check lines of code instead of actual functionality.

When Code Coverage Becomes a Problem

• Chasing 100% coverage: Some teams treat full coverage as a goal, writing tests just to hit a number rather than ensuring correctness.
• Testing trivial code: Writing tests for getters, setters, and one-liner functions adds noise without real value.
• False sense of security: High coverage doesn’t mean well-tested software — poorly written tests can still let critical bugs slip through.

Why This Madnesssssss?

• Management mandates high coverage: Non-technical stakeholders see high numbers as a success metric, even if the tests are ineffective.
• Developers game the system: Writing shallow or redundant tests just to satisfy coverage tools.
• Fear of missing edge cases: Instead of focusing on meaningful tests, teams try to cover everything — even when it’s unnecessary.

A Smarter Approach to Testing

• Prioritize meaningful tests over coverage numbers. A well-tested critical function is more valuable than 100% coverage on trivial code. Write tests that pass acceptance criteria and if possible check with your QA’s on the scenarios to test.
• Focus on edge cases and real-world scenarios. Test what can break, not just what exists.
• Use code coverage as a guide, not a rule. If coverage is low, ask why — but don’t blindly chase numbers.

A project full of useless tests doesn’t make it more reliable — it just makes refactoring harder and wastes development time.

Skimping on Quality Over Time

Many projects start with a strong emphasis on quality — clean code, proper testing, and good documentation. But as deadlines pile up and pressure increases, quality often takes a backseat. What starts as a few “temporary” shortcuts eventually becomes the norm, making the codebase harder to maintain and scale.

How Quality Slips Over Time?

• Rushed fixes become permanent solutions. “We’ll clean this up later” rarely happens, and quick hacks stay in production.
• Tech debt accumulates without being addressed. Small compromises add up, leading to a system that’s fragile and difficult to change.
• Code reviews turn into rubber-stamping. When teams are under pressure, reviews become a formality instead of a real safeguard.

Why This Happens?

• Deadlines take priority over maintainability. Short-term wins come at the cost of long-term headaches. Once quality is sacrificed for speed, it’s hard to go back, and “Good enough” becomes the standard.
• Lack of accountability. No one wants to be the one slowing things down, so bad practices get ignored.

How to Keep Quality High?

• Stick to coding standards, even under pressure. A rushed job today leads to bigger problems tomorrow.
• Schedule time for refactoring and tech debt. Make it a priority, not an afterthought.
• Encourage a culture of quality. Quality shouldn’t be optional — it should be an expectation at every stage.

Poor decisions might help you move fast now, but they will eventually slow you down. And before you know it, you’re stuck in the same cycle — rushing to meet deadlines while dealing with the consequences of past shortcuts.

I know what you’re thinking: “Who am I to say all this? I’m just some random person on the internet. What do I know about the complexity of a development process?”

Well, here’s the thing — it doesn’t need to be complex. Software development will always have its challenges, but making smart decisions early and correcting mistakes before they snowball can save you from the endless cycle of tech debt and frustration. Follow the right path, and if you deviate, try to correct it before you take on the next journey.

Stay Curious. Adios 👋

This article was originally written on Medium.