Poor Code Quality:
Causes, Risks, and How To Fix It

Software that seems to work well and appeals to users can still be written badly. According to Forbes’ 2025 Quality Transformation Report, 63% of surveyed organizations deploy code without thoroughly testing it first, and 45% favor speed over quality. This approach leaves you running the risk of only starting to fix poor code quality when it begins impacting your product.

Our article will explain how to recognize poorly written code, what causes it, and how it can damage your business. We’ll also talk about best practices that can help improve the codebase.

Code quality describes how well code supports your product and how safely it can adapt to future changes.

Poor code quality means having software that is hard to read, poorly secured, hard to extend, or easy to break—or all four. The difference between bad and high-quality code lies in these traits:

Readability

Understandable without massive decoding

Reliability

Behaves consistently under real-world conditions

Efficiency

Uses computing resources well enough
to stay responsive

Security

Has fewer avoidable openings for attackers

Testability

Easier to test specific behaviors

Changeability

Allows you to fix, update, and extend the software without breaking it

Reusability

Uses the same solution or rule in several places in the codebase

Top-grade software can have a lot of low-quality code in it. But as the codebase grows and quality dips, signs of problems can emerge.

The following signs can help you decide if your codebase needs a source code review before they affect your software and business.

Unstable behavior and bugs

The software behaves unexpectedly during normal use or after minor updates. Any product can have code smells, but poor code often causes bugs that return, appear in predictable user actions, or trigger failures in connected features.

Small changes breaking unrelated features

A small update that breaks an unrelated feature usually means the code is too tightly connected. The team changes one rule, fixes one screen, or adjusts one integration, then another feature starts behaving differently.

Hidden dependencies

Poorly written software often has hidden dependencies, meaning one part quietly relies on settings, data formats, services, or workflows it should not depend on. If developers don’t know about these dependencies, they may affect long-term maintainability as changes break other parts of the software.

Slow performance after growth

A poorly coded application can slow down as you add users, features, records, or integrations. This can appear as slow page loads, delayed searches, frozen screens, or resource-heavy background tasks. Often, this happens because the code worked on smaller datasets but was not built to handle higher volumes.

Inconsistent coding style and naming

The software may run, but the codebase will feel like several systems stitched together, with a mess of rules for names, formatting, file structure, error handling, and logging—usually for different programming languages and projects. Developers may find different rules across modules, slowing reading and review time.

Codebase is hard to understand

Badly made software is hard to figure out due to vague names, long files, mixed responsibilities, and old patches. It lacks comments and documentation, so routine maintenance can feel like an investigation.

Oversized functions and classes

Functions and classes become risky when they handle too many jobs at once. For example, one function may validate input, calculate prices, save records, send messages, and call an external service. Each task may make sense on its own, but together they create a dense block in which a single edit affects several workflows.

Duplicated code

Duplicated code means identical or nearly identical logic is copied and pasted in several places. This makes bugs harder to remove from the software because the same code quality liabilities remain in copied logic elsewhere.

Symptoms help you find weak areas in your code, but to deal with them, you have to be aware of the causes.

Poor code quality often stems from decisions that solve immediate problems without considering their long-term impact. Based on our code audits, the following causes are often the source of problematic and badly made code:

The same problems that introduce technical issues can affect your entire business.

Low-quality code doesn’t affect only the engineering team. You can feel the impact and damage in different parts of your business.

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  Increased operational and system costs.

  When code is inefficient because it repeats database calls, loads too much data, or uses server resources poorly, it takes more resources to run workflows. Forbes’ 2025 Quality Transformation Report found that 40% of companies say poor enterprise software quality costs them over $1 million per year, with 45% losing over $5 million.

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  Slower feature delivery.

  A poorly written and maintained codebase means new features take longer to add because developers have to investigate every piece of logic scattered across files or linked to unrelated workflows.

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  More security vulnerabilities.

  Messy logic, weak access checks, old dependencies, and unclear data handling increase the chance of data breaches. According to IBM’s 2025 Cost of a Data Breach Report, the average cost of a breach reached $4.4 million in 2025.
  

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  Weaker team productivity.

  A low-quality codebase will force developers to investigate confusing code, fix old problems, and worry about the side effects of each change. In turn, companies typically push for improved efficiency and faster delivery as key indicators of high-quality software, despite the drawbacks (per Forbes’ report).

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  Complicated onboarding.

  New recruits will need more time and senior developers’ help to become proficient in messy code. Hiring more developers won’t help much either because the codebase itself prevents them from contributing quickly.

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  Lower reliability for customers.

  User experience will suffer because of broken workflows, slow app loads, and constant failures. This could increase support tickets, add to churn rates, and reduce conversions.

You can’t fix a weak codebase between feature releases. Teams need a repair plan and should approach the problem progressively.

It’s safest to gradually improve the code quality, starting with areas where defects could hurt your business the most.

➤ Audit the codebase to rank high-risk areas

An audit lets you understand which parts of the code to focus on first. A big chunk of code may look ugly and not affect anything critical, but a smaller module may deserve more attention because it affects key workflows, like permissions or payments. The audit should end with a ranked list of issues, the reason each issue matters, and the safest order of repair.

➤ Clarify business and technical requirements

Developers need exact rules before they can write or improve the code. A requirement should exclude guesswork by explaining the business goal, affected user roles, normal flow, failure flow, limits, as well as code quality metrics that will be used as acceptance criteria.

➤ Define coding standards

Enforce conventions for names, folder structure, function size, documentation rules, shared logic placement, and other rules. Store editor configuration files in the repository so formatting and style settings stay consistent across the project.

➤ Supply developers with examples

Use concrete examples when business logic can be misunderstood. For example, a discount feature should define who qualifies, whether discounts can be combined, how refunds work, what reports should show, and what message appears when a coupon has already been used.

➤ Stabilize critical behavior with automated tests

Automated tests confirm that the software still behaves correctly after a change. A practical test pyramid uses many small unit tests for individual pieces of logic, fewer integration tests for connected parts, and a smaller number of full workflow tests for critical user paths.

➤ Refactor in smaller iterations

Refactoring in smaller batches lets developers improve one risky area, test the behavior, release the change, and continue. Avoid broad rewrites unless targeted repairs cannot make the system stable or safe enough to maintain.

➤ Assign ownership for critical modules and decisions

Make sure every module has an owner who’s responsible for keeping it readable, documented, tested, and secure. Not every module needs a separate owner, like a project has a product owner, but critical workflows like billing and reporting usually do.

➤ Limit dependencies between modules

Divide complex features with high coupling into smaller modules with clear responsibilities, separate the modules by responsibility, and wrap third-party tools inside adapters. This will decrease software complexity and minimize the risk that one change will affect unrelated features.

➤ Keep documentation close to code

Documentation doesn’t repeat obvious lines, but instead explains what future developers cannot infer from the code, like module purpose, business rules, inputs, outputs, failure behavior, or external dependencies. Keep this documentation in the same repository when possible, so developers can update it with the code and track changes over time.

A codebase usually becomes deficient from a series of continuous shortcuts, poorly thought-out decisions, and a lack of standards.

For DevCom, understanding causes helps decide how to optimize the product. We can audit your code for issues that slow delivery, amplify risks, complicate maintenance, or make new features harder to release.