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Technical debt refers to the implied cost of additional work caused by choosing easier, short-term solutions over more efficient, long-term ones when developing software. While technical debt isn't inherently bad, accumulating too much of it can slow development, increase costs, and lower the quality of the product.
Below are some common causes of technical debt:
Rushed Delivery or Tight Deadlines
When teams are pressured to deliver features or products quickly, they may cut corners, skip best practices, avoid refactoring code. This can lead to inefficient or unmaintainable code that needs to be reworked later, accumulating technical debt.
Example: Choosing a quick workaround to meet a launch deadline instead of implementing a more scalable solution.
Lack of Documentation
When developers fail to document code or architecture decisions, future developers may struggle to understand and maintain the system. This lack of documentation increases the likelihood of introducing errors or redundant solutions, adding to technical debt.
Example: A new team inherits a poorly documented codebase, which slows development and requires significant time to refactor.
Poorly Defined Requirements
Unclear or rapidly changing requirements can cause developers to implement solutions that aren't aligned with the final product's needs. As the product evolves, teams often need to go back and modify the code, increasing technical debt.
Example: Building features based on ambiguous requirements that change later, resulting in wasted development time and rework.
Lack of Refactoring
Refactoring is the process of improving code structure without changing its functionality. When developers neglect refactoring, especially after multiple updates, the code can become bloated and difficult to maintain, which leads to technical debt.
Example: A module that has grown in complexity over time is left unrefactored, making future enhancements more difficult.
Over-Reliance on Legacy Code
Legacy systems or outdated technologies can contribute to technical debt because they often lack flexibility and scalability. Maintaining or extending legacy code without updating the underlying architecture increases the burden of technical debt.
Example: Continuing to build new features on an outdated codebase that wasn't designed to support modern requirements.
Inadequate Testing
Skipping or underinvesting in testing leads to bugs and vulnerabilities that are difficult to trace and fix later. This can increase the cost of maintaining the system and introduce more technical debt over time.
Example: Deploying a feature with minimal unit testing and then spending significant time fixing bugs in production.
Poor Collaboration Between Teams
Inconsistent communication between development, design, and product management teams can result in misaligned goals. This can lead to duplicated work or features that don’t align with the broader vision, increasing technical debt.
Example: A development team builds a feature based on outdated requirements from the product team, which later requires a complete overhaul.
Use of Temporary Solutions
In some cases, teams may implement "quick fixes" or temporary solutions to solve immediate problems, with the intention of revisiting them later. However, these quick fixes can often become permanent, resulting in increased complexity and technical debt.
Example: Using hardcoded values to solve a bug quickly, with plans to replace them with a more flexible configuration system later—but never revisiting it.
Unskilled or Inexperienced Developers
Teams with less experienced developers may introduce technical debt unintentionally by writing inefficient or hard-to-maintain code. As the project grows, the shortcomings of these initial implementations can compound the technical debt.
Example: A junior developer creates a suboptimal solution due to lack of experience, and it becomes deeply embedded in the system.
Failure to Prioritize Technical Debt
Sometimes, technical debt is known but deprioritized in favor of delivering new features or meeting other business goals. Over time, this leads to a growing backlog of technical debt that becomes more expensive to address.
Example: A team knows that a certain subsystem needs refactoring but delays the work indefinitely in favor of delivering new features.