When speed is prioritized over quality, rushed development cycles often lead to poor architectural decisions and inadequate testing. This increases the likelihood of bugs and design flaws slipping through the cracks without proper quality assurance.

Even skilled developers can encounter unfamiliar technologies, and new hires may implement workarounds without proper guidance. These short-term fixes can increase system complexity over time.

Hard-to-read code or missing documentation slows development because the people who are trying to maintain it are also trying to decipher the original intent of it – often without the guidance of the original owner of the code. Without clear comments and supplementary documentation, developers must reverse-engineer the system, increasing the likelihood of errors.

Adding new features to a system that wasn’t designed for them increases the risk of technical debt. If developers aren’t aligned on requirements, last-minute fixes may compromise existing code stability. For example, if a project initially scoped for a basic reporting tool suddenly shifts to include real-time analytics, developers may implement hurried patches to accommodate the new metrics.

Outdated frameworks, limited documentation, and architectures incompatible with modern practices make updating systems time-consuming and increase the risk of introducing new bugs, especially without automated testing.

When an application is built as a single, tightly coupled system instead of independent components, scalability suffers, and updates become difficult. For example, an eCommerce platform built as a large codebase would require redeploying the entire system for every minor update.

Mixing architectural layers (e.g. user interface, business logic, and data access) increases maintenance complexity. For example, a reporting module that contains both database queries and UI code risks breaking functionality when either component is modified.

Embedding critical settings such as API endpoints or credentials directly in the code complicates deployment and increases the likelihood of errors, especially when different environments require manual adjustments.

Temporary workarounds that address symptoms rather than root causes, like adding multiple conditional checks instead of fixing an underlying, flawed algorithm, create long-term complexity.

A tangled, disorganized code structure results from incremental changes without a clear plan. Spaghetti code weakens the separation of concerns and can easily grow into a monolithic architecture that becomes difficult to manage.

Copying and pasting the same logic across different files or modules instead of creating reusable functions can lead to inconsistent maintenance. When changes to the software are required, every instance must be updated individually, and if even one is missed, this can cause potential vulnerabilities.

Complex logic should be accompanied by in-line explanations. If it isn’t, new developers have to guess at the logic’s intent.

When READMEs, API specs, or architectural diagrams are not updated, new developers may struggle to understand system workflows, increasing the risk of implementation errors

Without comprehensive unit, integration, or regression tests, developers can’t confidently merge or deploy new features.

Having tests that aren’t updated for evolving code creates a false sense of security.

Untested code paths can prevent developers from detecting hidden bugs or regressions after making changes.

Relying on manual processes (like FTP uploads) slows development and invites human error. This becomes a bottleneck when frequent feature releases are necessary.

Failing to update frameworks and libraries can introduce security vulnerabilities and compatibility issues.

Poor communication structures, such as scattered Slack messages and email threads, can lead to missed bug reports and delayed fixes.

Skipping peer reviews allows errors and poor patterns to accumulate, turning small bugs into major vulnerabilities over time.

Team members lacking the necessary expertise in the technologies or domains they are working with may turn to workarounds or inefficient solutions.

When the only person who deeply understands a system, such as a senior developer, leaves without sharing valuable knowledge, maintaining the system becomes difficult.