Technical Debt Explained: A Comprehensive Guide for Software Teams

Have you ever found yourself and your team in a situation where, no matter what, you must deliver a piece of software in a rush to meet a deadline? To achieve this, you take shortcuts and cut corners, expediting delivery. All along, you’ve known that a lot would need future reworking, resulting in a hidden cost called technical debt.

But what is technical debt exactly, and how can it be dealt with? In this article, we’ll explore its nature, types, and effective strategies for technical debt management, prevention, and reduction.

What is technical debt or code debt?

Technical debt, also known as code debt, design debt, or tech debt, is responsible for an increase in software costs by up to 40%, according to recent studies. By definition, technical debt is the hidden cost of future rework required when developers prioritize an expedient delivery of a feature or project over quality. It naturally comes in the form of refactoring. While sometimes necessary to move projects forward, technical debt requires improvements down the road to keep the software working or make it scalable.

Types of Technical Debt

Now that you understand the meaning of technical debt, you must know how it presents itself. Let’s explore 5 types of technical debt and its unique challenges:

Deliberate vs. Accidental Debt

• Deliberate debt:
  Deliberate debt happens when development teams intentionally sidestep best practices or necessary processes, such as skipping tests or logging in documentation, to meet tight deadlines.

• Accidental debt:
  Accidental debt, on the other hand, is unintentional and originates from inadequate planning, insufficient knowledge, or evolving project requirements. This type of debt, while not planned, accumulates over time as initial design decisions become outdated or technical issues emerge.

Code debt

Code debt is the type of technical debt that stems from suboptimal coding practices that create complex, unmanageable, and fragile codebases. Here are 4 of its key aspects:

1. Spaghetti Code: Spaghetti code is a term used to describe code that is intertwined, disorganized, and convoluted, lacking in coherence. It isn’t easy to follow, understand, maintain, and modify. It typically results from adding features without proper architectural planning, leading to paths that are so tangled that resemble a bowl of spaghetti. This type of code debt increases the chance of bugs and complicates debugging and enhancements. To tackle these issues, developers must engage in regular refactoring to clean up and streamline their code. Learn more about the importance of structured code refactoring to manage spaghetti code effectively.
2. God Class: God class or Gob object refers to large classes that handle various tasks and responsibilities, bloating the system and making it hard to maneuver. They’re a nightmare for maintenance as they’re highly interdependent, making the code rigid and challenging to test or build on since any change that is made can have generalized and unintentional results. Refactoring these classes into smaller and more focused classes will significantly improve code maintainability.
3. Magic Numbers: Magic numbers are random numbers scattered all over the code. They have no context and offer no explanation. Future developers have an even harder time understanding their value, which reduces code legibility and maintenance. Replacing magic numbers with meaningful constants or variables helps to keep code clarity.
4. Copy-Pasta: While copying and pasting code may seem like a quick solution, it leads to inconsistencies and redundancies throughout the code. Not to mention having to go over the copied code every time there’s an update. Not only does it make maintenance extremely difficult, but it also increases bug potential. Instead, create functions or reusable modules to keep your code DRY (Don’t Repeat Yourself).
   

Architectural Debt

Architectural debt is characterized by the failures and deficiencies in the fundamental structure of a software system. It’s the most dangerous type of debt since it is profoundly integrated into the system, which makes it hard to identify and even harder to correct. It manifests itself in 5 different ways:

1. Monolithic Architecture: A traditional software design model where all components are integrated into a single codebase. This includes user interface, business logic, and data management, all tightly coupled. While it initially simplifies development and deployment, the more the application grows, the more complex it becomes to maintain. Modifying or updating any part of the system requires redeploying the entire application, which can be risky and lengthy. Since it is so inflexible and there’s no way to adjust specific parts independently, scalability is almost impossible. As time goes by, efficiency and adaptability are reduced, creating architectural debt.
2. Excessive Coupling: Excessive coupling is a condition where all software components are excessively dependent on one another. This interconnection creates a fragile and rigid system where changes made in a single component might have general and unintentional effects on the others, requiring alterations and adjustments to various parts of the system. This leads to higher maintenance costs and higher bug risk.
3. Lack of Modularity: When software lacks modularity, its components are not sufficiently decoupled, well-defined, and independent, often resulting in a rigid and monolithic structure. This type of structure has a higher error propensity and lower scalability, due to the developers’ inability to isolate and modify individual components. When there’s a single change, the entire system needs to be extensively reviewed, increasing software costs.
4. Obsolete Design Patterns: As software design evolves, so do languages, frameworks, and paradigms. And as these evolve, certain design patterns become obsolete. Such is the case with the Prototype and the Single patterns, not to mention the Decorator and the Chain of Responsibility patterns. These patterns have fallen into the abyss of obsolete design patterns, being seen now as redundant and less relevant. Since they’re outdated, they have been replaced by more modern constructs. However, continuing to use these obsolete design patterns may lead to the rise of technical debt, making code refactoring more challenging and increasing the overall complexity of the system.
5. Lack of Architectural Documentation: Inadequate and improper documentation may seriously and significantly impact software development. This happens because of how crucial effective documentation is for the success of any project, as it ensures the team understands, knows, and can follow the project’s direction and objectives. When it is lacking, critical information is lost, especially when team members leave or are replaced. This knowledge gap when it comes to the system can cause a misalignment of project goals, delays, errors, and ultimately, failure.
   

Test Debt

Test debt emerges from insufficient, inadequate, or bypassed testing, which happens normally under the pressure to expedite delivery. This type of debt accumulates as developers push untested code to production, releasing a minimally piece of working software that appears functional but could be filled with bugs due to a fragile and poorly constructed codebase. Some signs that test debt is accumulating include insufficient test coverage, reliance on manual testing, and high rates of bugs post-release. One effective way to mitigate test debt is through regular and thorough code reviews. Discover why code reviews are essential for maintaining code quality and preventing test debt.

Documentation Debt

Documentation debt is a type of technical debt in software development that stems from the neglect of proper documentation practices. Much like the type of debt we described in Lack of Architectural Documentation, this type is related to malpractice when it comes to documenting not only the architectural parts of the project, but all essential information, including code comments, API documentation, and user manuals. When documentation is either missing or outdated, developers face significant challenges, especially during team transitions or onboarding. This lack of comprehensive documentation leads to debt, as it results in delays and increased errors. For strategies to improve your software documentation practices, read our guide on software technical documentation.

The Impacts of Technical Debt on Development Efficiency, Quality and Cost

The burden of technical debt on development is substantial, affecting costs, efficiency, and overall project viability. McKinsey’s research shows that technical debt can account for up to 40% of IT balance sheets, with some companies spending 15-60% of every IT dollar on managing this debt​. Understanding and mitigating the impacts technical debt can have ensures smoother project execution and sustainability. Let’s take a look at 6 of them:

1. Increased Costs: As technical debt accumulates, maintenance and resolution costs rise, making the project more expensive.
2. Reduced Efficiency: Since developers must spend more time on fixes and modifications, development slows down.
3. Decreased Quality: The higher the technical debt, the more bugs and failures there will be, reducing software quality and reliability.
4. Limited Scalability: Similarly, the higher the technical debt, the harder it is to scale the software since future updates and enhancements are a challenge.
5. Team Morale: Developers dealing with persistent technical debt tend to become frustrated and may burn out, increasing turnover.
6. Customer Satisfaction: Technical debt causes delays, bugs, and performance issues, negatively impacting customer satisfaction and retention.

Recognizing and addressing these impacts through proactive management is fundamental to ensure project success and viability. By understanding the repercussions of technical debt, you can better strategize on preventing it from accumulating in the first place. This leads us to our next focus: preventing technical debt.

Can you prevent Technical Debt

While eliminating technical debt is challenging, preventing it is achievable through strategic measures.

First, education and training are crucial in preventing technical debt. Regularly provide your team with education and training on what technical debt is, how to manage it, and the best practices for doing so. Ensuring that everyone is aware of the impacts and management strategies for technical debt is essential for prevention.

Next, thorough planning is necessary. Allocate time for meticulous project planning and design. This helps to avoid shortcuts that can lead to technical debt. Comprehensive planning helps in foreseeing potential issues and preparing solutions in advance, thus minimizing debt accumulation.

Regular code reviews are also vital. Conducting frequent code reviews allows for early identification and resolution of potential issues, preventing technical debt from building up. These reviews ensure that the codebase remains clean and maintainable.

Moreover, comprehensive testing plays a significant role in preventing technical debt. Implementing thorough and automated testing strategies helps in the early detection of problems. Addressing these issues promptly prevents them from escalating into significant technical debt.

Lastly, robust documentation is essential. Keeping all documentation detailed and up-to-date reduces the chances of errors and misunderstandings. It also facilitates the smooth integration of new team members, ensuring continuity and reducing the risk of accumulating technical debt.

By adopting these strategies, teams can effectively prevent the onset of technical debt and maintain a healthy codebase.

However, prevention alone may not be enough, as existing technical debt often needs to be addressed. To manage it properly, you first need to measure it accurately. This brings us to our next section on how to measure technical debt effectively.

How to Measure Technical Debt

To measure technical debt, assess it using metrics like Technical Debt Ratio (TDR), code quality metrics (complexity, coverage, duplication), maintainability index, and code smells. These metrics provide insights into the cost and impact of suboptimal code, guiding improvements for a healthier codebase.
While some argue that technical debt can’t be accurately measured due to its inherently abstract nature, it is possible to quantify and manage it with the right approach and tools. Here’s how you can measure technical debt like a pro:

Technical Debt Ratio (TDR)

The TDR compares the cost of fixing the software (remediation cost) to the cost of building it (development cost). A lower TDR indicates better quality and maintainability. Tools like SonarQube can calculate TDR by analyzing the codebase for issues and estimating the remediation effort. Integrating TDR into regular CI/CD pipelines helps monitor and address technical debt continuously.

Code Quality Metrics

Some code quality metrics can indirectly measure technical debt:

• Code Complexity: High cyclomatic complexity often signals potential technical debt, as complex code is more challenging to understand and maintain. Tools like SonarQube and Code Climate provide detailed complexity analysis. Understanding and reducing code complexity is crucial for maintaining a healthy codebase.
• Code Coverage: Low test coverage indicates higher technical debt, as untested code is more prone to bugs. Coverage tools like JaCoCo, Istanbul, and Coveralls help track and improve this metric. Comprehensive test coverage is a key factor in mitigating technical debt.
• Code Duplication: Duplicated code increases maintenance effort and signals technical debt. Tools such as PMD, Checkstyle, and SonarQube can identify and report code duplication. Regular refactoring to reduce duplication is essential for maintaining a clean codebase.

Maintainability Index

This index is a composite metric derived from various code metrics, including cyclomatic complexity, lines of code, and Halstead volume. A higher maintainability index indicates a more maintainable and less debt-ridden codebase. Tools like Visual Studio, NDepend, and SonarQube calculate maintainability indexes. This index helps prioritize refactoring efforts.

Code Smells

Code smells are indicators of potential technical debt within the codebase. They include issues like long methods, large classes, and excessive parameter lists. Identifying and addressing code smells can reduce technical debt. SonarQube, PMD, and FindBugs are tools that can help detect code smells. Incorporating regular code smell analysis into the development workflow is crucial to maintaining code quality.

By leveraging these metrics and tools, development teams can gain a clear understanding of their technical debt, prioritize remediation efforts, and implement strategies to maintain a sustainable codebase. Consistent monitoring and iterative improvements are crucial for effective technical debt management.

Now that we’ve covered how to measure technical debt, let’s explore some essential tips and tricks for managing it effectively.

Managing Technical Debt: 8 Essential Tips and Tricks

Effectively managing technical debt is vital for the maintenance of a healthy, robust, and scalable codebase. In the end, it all comes down to actually addressing it. But how? Check out these 8 strategic steps that will help you successfully handle technical debt in your projects:

1. Identify and Log Technical Debt:
   • Identify: Review your code regularly using tools such as SonarQube, ESLint, and CodeClimate. These tools can help detect and quantify technical debt, highlighting areas with code smells, duplications, and inefficiencies.
   • Log: Document all identified technical debt in a centralized and accessible system. Use tools like Jira or Confluence for the documentation, making sure to include detailed descriptions, impact assessments, and next steps.
     
2. Categorize and Rank Debt:
   • Categorize: Classify technical debt into debt categories such as code debt, architectural debt, test debt, and documentation debt. Use tags and filters in your project management tools to organize and manage these categories and also classify them as performance issues, security vulnerabilities, and maintainability challenges.
   • Rank: Focus on high-impact debt that represents the greatest risk to project success and longevity using frameworks like the Eisenhower Matrix.
     
3. Plan and Implement Debt Resolution:
   • Plan Debt Resolution: Schedule regular sprints for resolving technical debt. Include these in your project plans.
   • Track Progress: Use metrics such as Code Coverage, Technical Debt Ratio, and Maintainability Index from tools like SonarQube and CodeScene to monitor technical debt resolution progress. Review these metrics regularly.
     
4. Establish and Enforce Coding Standards:
   • Establish Standards: Develop and constantly update coding standards to reflect best practices and prevent the introduction of new technical debt. These should cover naming conventions, code formatting, and documentation.
   • Enforce Standards: Implement code review and statistical analysis tools, such as Prettier, ESLint, and StyleCop, to ensure adherence to the established coding standards. Conduct regular peer reviews to maintain high code quality.
     
5. Regular Code Refactoring:
   • Refactor Regularly: Make code refactoring a continuous development process. Use IDE tools like IDEA and Eclipse for automated refactoring.
   • Schedule Refactoring: During sprint planning, make sure to balance refactoring with the development of new features. Schedule regular refactoring tasks to improve code quality.
     
6. Incorporate Automated Testing:
   • Extensive Test Suite: Develop and uphold a detailed suite of automated tests, including unit integration, and end-to-end tests. Adopt frameworks such as Jest, Mocha, and Cypress for extensive test coverage.
   • Ongoing Testing: Integrate ongoing testing practices within your CI/CD workflows using tools such as Jenkins, Travis CI, and GitLab Ci/CD. This practice helps identify problems early, maintaining code quality high.
     
7. Integrate CI/CD Practices:
   • CI/CD Setup: Create CI/CD pipelines to automate the process of code integration and deployment. Tools such as Jenkins, CircleCI, and GitLab CI/CD ensure streamlined feature delivery and enhance reliability.
   • Monitoring and Optimization: Review and refine CI/CD pipelines. Use monitoring tools like Prometheus, Grafana, and ELK Stack to track performance metrics and drive data-based adjustments.
     
8. Schedule Technical Debt Sprints:
   • Focused Sprints: Organize sprints that are focused mainly on addressing technical debt. This practice keeps the codebase clean and organized.
   • Dual Focus: Strike a balance between new feature development and introduction and technical debt management and reduction. Make sure technical debt reduction is a core aspect of the development cycle.

Effectively managing technical debt is vital for maintaining a robust and scalable codebase. By following these strategic steps, you can ensure that your development process remains efficient and your software maintains high quality. Remember, proactive management, continuous monitoring, and regular improvements are key to staying ahead of technical debt and sustaining long-term project success.

Conclusion

All things considered, while technical debt can impact your project’s success, it does not have to be daunting and it doesn’t have to haunt you either. Effective steps and strategies can be implemented and adopted in order to not only manage old debt but especially to prevent new debt from occurring. Through educating yourself and your team and taking proactive measures, you will be able to address and reduce existing debt all the while avoiding new accumulation. Prioritizing these steps and committing to them will ensure the long-term success of your projects through the maintainability and scalability of your codebase. Embrace these strategies and you will see technical debt no longer as a burden but as a manageable and organizable part of your development process.

Ready to tackle technical debt? Implement these strategies and you’ll see the difference it will make to your codebase, making it scalable and maintainable!

Luciana Pirk

With a decade of experience in remote work, Luciana Pirk is the Content Manager at DistantJob. She combines her expertise in SEO copywriting with leadership skills in managing remote teams. Luciana is dedicated to promoting remote work, focusing on mental health and enjoying activities like meditation and yoga in her free time.