The Hidden Costs of Poor Code Quality And How to Eliminate Them

A 2018 Stripe and Harris Poll report revealed that developers spend nearly 42% of their week dealing with technical debt and poor code (including 3.8 hours on debugging “bad code”). That inefficiency represents an estimated $85 billion in lost productivity each year.

The impact of bad code does not end with poor practice; it ultimately leads to extra debugging, slower reviews, riskier releases, and rising incident load. In this article, we will discuss the negative impact poor code has on teams and what engineers can do to prevent it from becoming a bigger issue.

What Is Poor Code Quality, and Why Does It Matter?

The term ‘poor code quality’ encompasses not just the odd typo or forgotten comment, but far more systemic issues. It covers the most important aspects of software engineering: how code behaves when it is changed, when deadlines are tight, and when multiple people are working on it at once.

Bad code tends to be fragile, risky, and even dangerous to handle. It slows down the process, introduces errors, and even minor changes can feel like a gamble. You’ll often see it in forms like:

• Spaghetti code: Logic tangled up with tight dependencies and shortcuts that make even small changes risky.
• Lack of tests: There is no efficient method to identify the issues before they reach users.
• Inconsistency of style: The coding style in one file looks and feels different than that in another, making it more difficult to maintain.
• Hidden dependencies: Modules can have hidden interdependencies that cannot be seen, and as a result, refactoring is fragile and unpredictable.

Clean, testable, and well-organized code allows teams to:

• Iterate quickly without breaking things
• Onboard engineers faster
• Detect bugs early
• Scale systems with confidence

This is why code quality management is important. It’s the foundation of reliable, sustainable software development.

The Hidden Costs of Poor Code Quality

Poor code quality doesn’t always result in failure, but it ends up costing teams subtly over the long run. Below, we’ll break down where these hidden costs come from and how they impact your team and product.

1) Slower development velocity

Messy, unclear, or tightly coupled code means that developers tend to spend more time trying to understand it instead of writing new logic. This particularly damages onboarding, where recruits struggle to get up to speed. Even experienced engineers find themselves stuck in cycles of reading, tracing, and testing before they can safely make a change.

Research from CodeScene shows that:

• Teams working in low-quality (or “Red”) codebases experience up to a 124% increase in development time.
• Most of that time is spent dealing with defects, uncertainty, and navigating fragile, complex code areas.

In these environments, adding a small feature can take days instead of hours.

2) Rising maintenance and support costs

Badly written code is fragile. Once it breaks, it is not at all a straightforward thing to fix. When there is no good test coverage and structure, it all looks like a guessing game every time a bug is fixed. No matter how minor a problem may seem, it can lead to a series of far-reaching ripple effects.

What this looks like in real terms:

• A simple UI bug leads to backend changes.
• A regression fix breaks another feature.
• Teams spend hours triaging and hotfixing the same modules again and again.

Example cost formula:

• 100 bugs per month × 2 hours per bug × $100/hour = $20,000/month

And that’s just the engineering time. It doesn’t include late-night deployments, where developers scramble to fix broken features before release. Or the time support teams spend handling frustrated users, chasing tickets, and feeding issues back to engineering, creating a constant loop of firefighting.

3) Technical debt accumulation

Not all bad code starts out bad; often it’s the result of time pressure. Quick fixes, hardcoded values, and one-off logic are used to meet deadlines, but are rarely revisited later. Over time:

• The backlog fills with refactor tickets, and no one has time to address them.
• Legacy modules become so tangled that no one wants to touch them.
• Teams stop improving code because the risk feels too high.

This is how you end up in a debt trap. New features slow to a crawl because the foundation can’t support change. Even small improvements require major effort.

4) Defect escapes and customer impact

Bugs happen. But in low-quality codebases, they’re more frequent and more dangerous. Lack of testing, unclear logic, and unpredictable side effects make it harder to catch issues before they reach production. This results:

• Downtime during peak usage hours
• Broken user journeys that block conversions
• Security or compliance violations that lead to legal risks

5) Team morale, burnout, and turnover

When poor code quality turns every sprint into damage control, team motivation drops fast.

• Developers feel stuck doing cleanup instead of creating value.
• On-call becomes stressful, with recurring incidents and no long-term fix.
• Team retros become repetitive, with the same pain points resurfacing.

According to the 2024 MediaWiki developer survey, 66.5% of developers reported that poor code quality negatively affected their productivity, while 70% pointed to technical debt as a direct source of disruption.

6) Opportunity cost

Every bug fix, refactor, or delay caused by bad code is valuable time lost on development. The real loss is in what could have been delivered: better features, faster releases, and more competitive products.

What poor code quality prevents:

• Shipping customer-requested features faster
• Improving performance or usability
• Experimenting with new ideas or tools

Innovation slows down because engineering time is locked up in maintenance. And the longer teams wait to address quality issues, the more opportunities they miss.

How To Identify and Fix Code Quality Issues

Once you understand the damage caused by poor code quality, the next step is learning how to spot it. These issues don’t always show up clearly. They tend to build gradually, quietly dragging down team efficiency and product stability.

Here are some common red flags to watch for:

• Pull requests take days because no one wants to review them
• The same modules keep causing production bugs
• Large, unstructured files with no clear ownership
• Missing or minimal test coverage
• Areas in the codebase with high churn in version control

It is one thing to identify the signs, and another to act upon them. Teams should make iterative improvements during development before it’s too late.

Some practical steps that make a real difference:

• Set clear standards: Establish a style guide as a team and enforce it through linters.
• Effective code reviews: Ensure code reviews cover code readability, test coverage, and maintainability, not just whether the code functions properly.
• Make testing part of the culture: Invest in unit and integration tests and run with each build.
• Refactor often: Do not wait to rewrite the code. Regular periodic cleaning is far better in the long term.

Through these steps, teams can slowly decrease friction, increase stability, and improve the ease and safety of working with the codebase.

Tools and Best Practices To Improve Code Quality

Although no amount of technology can ensure clean code, the right tools and processes can enable teams to identify problems early, be more consistent, and improve over time.

Sample tools that support high-quality development:

Best practices to reinforce these tools:

• Integrate tools into your CI pipelines: Make sure that every pull request undergoes static analysis, linting, and testing.
• Adopt branching policies and review thresholds: Implement policies that enforce review, testing, and quality checks of the code prior to merging into main branches.
• Blameless postmortems: When issues escape into production, treat them as learning opportunities. Use those findings to tighten quality checks and improve automation.
• Automate and standardize: Let machines handle the tedious, repetitive jobs that can be easily standardized, such as style enforcement, testing, and deployment checks, allowing engineers to focus on building.

These tools, when combined with considerate engineering practices, allow teams to uphold high standards and identify problems before they are passed on to users.

Conclusion

One of the biggest myths in software engineering is that code quality is optional. In reality, it’s a multiplier; it speeds up delivery, reduces risk, and supports long-term growth. The hidden costs-slow development, rising maintenance, technical debt, burnout, and missed opportunities-don’t just add up; they compound over time.

Improving software code quality isn’t about perfection. It’s about making smart, intentional decisions that help teams move faster and build more reliably. It’s not overhead; it’s an investment that pays off.

Here’s your takeaway:

• Treat code as a product, not just a means to an end.
• Start small: refactor one thing per sprint.
• Automate what you can; review what you can’t.
• Champion quality not because it’s cleaner, but because it builds faster, stronger teams.

Let’s build code we’re proud of, one commit at a time.