15 Common Technical Debt Examples with Solving Guidance and Tips on How to Avoid

Software development shortcuts

In the world of software, we sometimes take shortcuts to get things done faster. These shortcuts, known as "technical debt," can pile up and make future work harder, much like unpaid bills. In this article, we'll explore different types of technical debt, why they happen, and how to handle them. Think of it as a guide to keeping your software projects clean and efficient. Let's dive in and learn how to avoid these common pitfalls!

1. Legacy code

Legacy code refers to old code that is no longer in line with current best practices or technologies but is still in use.

Reasons:

1. As technology evolves, older code may not efficiently utilize new features or practices.
2. Often, legacy code isn't regularly updated or refactored.
3. Original developers may have left, making understanding the code difficult.
4. ‍Legacy code often arises from rapid development cycles or lack of documentation.

Seriousness:

Very serious, as it can hinder future development and can introduce security vulnerabilities.

Consequences:

1. More time and resources are required for maintenance.
2. Difficulties in integrating with new technologies or systems.
3. Potential security risks.
4. Decreased agility in implementing new features.

How to spot it:

• Outdated language syntax
• Lack of recent updates
• Parts of the codebase that are rarely touched or understood by the current team
• Reliance on deprecated libraries
• Areas where updates are avoided due to fear of breaking things.

How to avoid:

‍Regularly refactor and update the codebase. Invest in continuous training for the team to keep up with current best practices. Encourage a culture of continuous improvement.

How to deal with it

‍Refactoring, which means restructuring existing code without changing its external behavior. Also, ensuring proper documentation and tests for the legacy code.

1. Gradually refactor the code.
2. Document as much as possible about the existing code.

Example:

‍Many banking systems still run on old mainframe systems written in COBOL. These systems are costly to maintain and difficult to integrate with modern technologies.

2. Insufficient testing

This refers to software that has been released with minimal testing or without comprehensive test coverage. It means not conducting enough tests to ensure the software works correctly under all scenarios.

Reasons:

• Rushing to meet deadlines may lead to skimping on tests.
• Lack of adequate resources or personnel for thorough testing.
• Underestimating the importance of testing.

Seriousness:

Very serious, as it can lead to undetected bugs in production.

Consequences:

• More bugs and issues in production.
• Reduced user trust and satisfaction.
• Software malfunctions.
• Security breaches.
• Loss of customer trust.

How to spot it:

• Frequent and unexpected bugs in production.
• Lack of automated tests.
• Areas of the codebase with low test coverage.
• Lack of comprehensive test cases.

How to avoid:

• Adopt a test-driven development (TDD) approach.
• Allocate sufficient time for testing in the development cycle.
• Use automated testing tools and continuous integration to catch issues early.

How to deal with it:

• Expand the range and depth of tests.
• Utilize automated testing tools.
• ‍Implement automated testing.
• Implement continuous integration.
• Dedicate resources to thorough testing.

Examples:

Financial sector: The 2012 Knight Capital trading glitch was due to untested software, leading to a $440 million loss in just 45 minutes.

E-commerce applications: Insufficient testing can lead to critical failures during high-traffic events like Black Friday sales.

3. Hard-coded values

Hard-coding refers to embedding specific values directly into the code rather than using variables or configuration files.

Reasons:

1. Often a result of attempting to solve problems quickly.
2. Not anticipating the need for these values to change.

Seriousness:

Moderate to serious, depending on the nature of the value and where it's used.

Consequences:

• Difficult to adapt the software for different scenarios.
• Increases the risk of errors when changes are needed.
• ‍Difficulty in updating or scaling the software.
• Potential security risks if sensitive information is hard-coded.

How to spot it:

• Direct references to specific values in the code instead of variables or configurations.
• Code reviews and static code analysis can help identify these.

How to avoid:

• ‍‍Use configuration files or environment variables for values that might change.
• Educate developers about the risks and downsides of hard-coding.

How to deal with it:

• Replace hard-coded values with variables or configurations.
• Implement strict code review processes to catch these issues.
• Refactoring to replace hard-coded values with variables or configuration files.

Example:

Weather Applications: Hard-coding location data can limit the app's usability in different regions.

‍An application that hard-codes database connection details would need to be manually updated and redeployed if the database location changes.

4. Outdated libraries or dependencies

Using old versions of third-party libraries or dependencies that are no longer supported or have known issues.

Reasons:

• Fear that updating may break existing functionalities.
• Developers may not be aware of newer versions.
• ‍Neglecting regular updates.
• Compatibility issues.

Seriousness:

‍Serious, especially if the outdated libraries have known security vulnerabilities.

Consequences:

• Older versions may have unpatched vulnerabilities.
• Newer versions often come with performance improvements.
• Security breaches.
• Software crashes.
• Imcompatibility issues.

How to spot it:

• ‍‍Dependency management tools can notify of outdated libraries.
• Regular audits of dependencies can also reveal this debt.
• Checking the project's dependencies against the latest versions.

How to avoid:

• ‍‍Regularly review and update dependencies.
• Use tools that can automatically check for outdated or vulnerable libraries.
• Educate the team about the importance of staying updated.

How to deal with it:

• Plan regular intervals for reviewing and updating dependencies.
• Utilize tools that alert about outdated dependencies.

Example:

Financial sector: The Equifax data breach in 2017 was due to an outdated version of the Apache Struts framework.

Web development: An outdated web framework can lead to security breaches.

5. Lack of documentation or outdated documentation

Absence of clear documentation for the code, making it difficult for others to understand or modify.

Reasons:

• Skipping documentation to meet deadlines.
• Assuming the code is self-explanatory.
• Lack of emphasis on documentation.

Seriousness:

Moderate to serious, depending on the complexity of the software.

Consequences:

• New team members find it hard to understand the system.
• Slows down bug fixes and feature additions.
• Increased development time.

How to spot it:

• ‍‍New team members struggling to understand the code
• Developers spending excessive time deciphering existing code rather than writing new code.

How to avoid:

• ‍‍Make documentation a required part of the development process.
• Use tools that facilitate documentation, like Doxygen for code or Confluence for architectural decisions.
• Encourage a culture where documentation is valued.

How to deal with it:

• Encourage writing documentation during the development process.
• Schedule regular reviews of the documentation.
• ‍Allocate time for documentation
• Use tools that encourage documentation.

Example:

API Development: Lack of documentation can make it difficult for external developers to use the API effectively.

‍A developer leaving the company without documenting their code can lead to weeks or even months of delay as the new developer tries to decipher the code.

6. Suboptimal architecture

The software's architecture doesn't support current or future requirements efficiently.

Reasons:

• Not spending enough time planning the architecture.
• The architecture may not evolve with changing requirements.

Seriousness:

‍Very serious, as it can hinder scalability and performance.

Consequences:

1. Difficulty in scaling the application to meet growing needs.
2. Inefficient architecture can lead to sluggish performance.
3. Increased maintenance costs.
4. Difficulty in adding new features.

How to spot it:

• ‍‍Performance bottlenecks, difficulty in scaling, or when adding new features becomes increasingly complex.
• Regular system reviews and architectural evaluations can also highlight areas of concern.

How to deal with it:

• Periodically review and assess the architecture.
• Be prepared to refactor parts of the architecture.
• ‍Re-architecting or refactoring critical parts of the system.

How to avoid:

• ‍‍Invest time in proper architectural planning at the outset of a project.
• Regularly review and adjust the architecture as requirements evolve.
• Adopt architectural patterns that are scalable and flexible.

Example:

Streaming services: An architecture that cannot handle high concurrent loads can result in service outages.

Startup: A startup that quickly grows its user base might find its initial database architecture can't handle the increased load, leading to slow response times.

7. Inconsistent coding standards

Different parts of the codebase follow different coding styles or standards.

Reasons:

• Different styles from various developers.
• Absence of defined coding standards.

Seriousness:

‍Moderate, as it affects code readability and maintainability.

Consequences:

• Makes the code harder to understand and maintain.
• New team members take longer to acclimate.
• Increased chances of bugs.

How to spot it:

• ‍‍Code reviews reveal varying coding styles or practices within the same codebase.
• Tools like linters or static code analyzers can also flag inconsistencies.

How to avoid:

• ‍‍Establish and enforce coding standards from the beginning of the project.
• Use tools like linters and formatters to automatically enforce these standards.
• Conduct regular code reviews to ensure adherence.

How to deal with it:

• Develop and distribute clear coding guidelines.
• Regular code reviews to ensure adherence to standards.
• Using tools like linters.

Example:

‍A project where some developers use tabs and others use spaces for indentation can lead to messy and hard-to-read code.

Open source projects: Often suffer from inconsistency due to contributions from a wide array of developers.

8. Deferred upgrades

Postponing necessary upgrades to the system or infrastructure.

Reasons:

• Limited time or budget to implement upgrades.
• Fear that upgrades might introduce new issues.
• Concerns about breaking changes
• Lack of resources
• Prioritizing new features over upgrades.

Seriousness:

‍Serious, especially if the deferred upgrades contain security or performance improvements.

Consequences:

• ‍Security vulnerabilities
• Older versions may be more vulnerable to attacks.
• Missing out on optimizations in newer versions.

How to spot it:

• ‍‍Regular checks of software and library versions against the latest available versions.
• Monitoring tools or dependency management tools can notify when updates are available.

How to avoid:

• ‍‍Set up a regular schedule for reviewing and updating dependencies.
• Use automated tools to notify when updates are available.
• Allocate time in the development cycle specifically for upgrades.

How to deal with it:

1. Create a detailed plan for regular upgrades.
2. Ensure thorough testing before deploying upgrades.‍
3. ‍Schedule regular upgrade cycles.
4. Have a rollback plan in case of issues.

Example:

‍A company that defers operating system upgrades might be exposed to security vulnerabilities that were patched in newer versions.

Corporate IT Systems: Delaying OS upgrades can leave the entire network vulnerable to security threats.

9. Tightly coupled components

Components or modules of the software are too interdependent, making changes in one component affect others.

Reasons:

• Initial design didn’t adequately consider separation of concerns.
• Gradual additions without revisiting the overall architecture.
• ‍Lack of modular design.
• Overlooking the principles of separation of concerns.

Seriousness:

‍Serious, as it affects maintainability and scalability.

Consequences:

• Modifying one component can affect many others.
• High risk of bugs when making changes.
• Increased testing efforts.

How to deal with it:

• Incrementally refactor to decouple components.
• Introduce interfaces and abstraction layers.
• Following design principles like SOLID.

How to spot it:

• ‍‍Changes in one module frequently cause unexpected issues in another.
• Code reviews and architectural evaluations can also identify areas where components are overly interdependent.

How to avoid:

• ‍‍Design with modularity and separation of concerns in mind.
• Follow design principles like SOLID. Use design patterns that promote loose coupling.

Example:

‍Changing a feature in one module of an e-commerce platform causes unexpected bugs in the payment processing module due to tight coupling.

A web application where changes in the database schema require significant changes in the business logic and UI layers.

10. Accumulated bug debt

Known bugs or issues are not addressed and keep accumulating.

Reasons:

• Prioritizing new feature development over bug fixing.
• Limited time or staff allocated to resolving bugs.
• Misjudging the effects of leaving bugs unresolved.
• Some bugs may be complex and time-consuming to fix.

Seriousness:

Can range from moderate to very serious, depending on the nature of the bugs.

<span class="colorbox1" fs-test-element="box1"><p>Accumulated bug debt can lead to unstable software, user dissatisfaction, and increased maintenance challenges.</p></span>

Consequences:

• Increased likelihood of crashes or failures.
• Deteriorating user experience and trust.
• Older bugs becoming harder to fix as code evolves.
• Potential for unaddressed bugs to pose security risks.

How to deal with it:

• Regularly review and prioritize bug fixing.
• Understand and address the underlying causes of bugs.
• Implement automated testing and CI/CD pipelines.

How to spot it:

• ‍‍A growing backlog of known, unresolved bugs.
• Regularly reviewing bug tracking systems and listening to user feedback can highlight this debt.
• Regular user complaints.
• Frequent patches.

How to avoid:

• ‍‍Prioritize fixing critical bugs over adding new features.
• Allocate dedicated time in each sprint or development cycle for addressing known issues.
• Use bug tracking tools effectively.

Example:

‍A software with known performance issues might lose users to competitors if those issues are not addressed in time.

Online Gaming Platforms: Ignoring bugs can lead to gameplay issues, causing player dissatisfaction and impacting the platform's reputation.

11. Not refactoring

Refactoring involves restructuring existing code to improve its readability, performance, or maintainability without changing its functionality. Not doing so leads to technical debt.

Reasons:

• Tight deadlines leading to postponement of refactoring.
• Concern that changes might introduce new bugs.
• Insufficient grasp of the benefits of refactoring.
• Resources focused more on new features than maintenance.

Seriousness:

Not refactoring can result in a codebase that is hard to understand, maintain, and extend, affecting long-term project health.

Consequences:

• The code becomes more complex and less maintainable over time.
• More time and resources required for future changes.
• Difficulty in scaling or adapting the software.
• New feature development hindered by existing code complexities.

How to spot it:

• Code with increasing complexity.
• Duplication.
• A decrease in performance or maintainability.

How to avoid:

• Schedule regular refactoring sessions.
• Foster a culture that values code quality as much as feature development.

How to deal with it:

• Refactor code in small, manageable parts.
• Use code reviews to identify areas needing refactoring.
• Tools that help identify refactoring opportunities.

Example:

• Retail software systems: Without refactoring, the code managing inventory might become too complex, making it hard to adapt to new product types or sales channels.

12. Ignoring deprecated libraries

Continuously using libraries or frameworks that are outdated and no longer supported or recommended for use.

Reasons:

• Concern that updating may cause compatibility problems.
• Lack of time or resources to replace or update the deprecated libraries.
• Developers may not be aware that certain libraries are deprecated.
• Resistance to change due to familiarity with the current libraries.

Seriousness:

Relying on deprecated libraries can lead to security risks, lack of support, and compatibility issues with other modern technologies.

Consequences:

• Deprecated libraries may not receive security updates.
• Challenges integrating with modern systems or software.
• Missing out on improved features of newer libraries.
• Difficulty in maintaining and troubleshooting.

How to spot it:

• Regularly check the status of libraries and frameworks used in the project for deprecation notices.

How to avoid:

• Stay updated with industry trends and library updates.
• Plan for regular reviews and updates of dependencies.

How to deal with it:

• Plan to gradually replace deprecated libraries with modern alternatives.
• Conduct regular audits of used libraries for deprecation status.
• Thoroughly test the system after replacing deprecated libraries.

Example:

• Web development: An outdated web framework might not support recent web standards, leading to compatibility issues with modern browsers.

13. Code duplication

This refers to having multiple segments of code that perform the same functionality or are very similar, often due to copying and pasting code.

Reasons:

• Copying code is quicker than creating reusable components.
• Developers may not realize code is being duplicated.
• Failing to consolidate similar code into reusable functions or modules.
• Different teams working in silos, leading to redundant efforts.

Seriousness:

Code duplication increases the maintenance burden, as changes need to be made in multiple places, and can lead to inconsistencies and bugs.

Consequences:

• More time spent on updating and fixing similar code in multiple locations.
• Fixes or improvements in one area may not be replicated in duplicated code, leading to inconsistent behavior.
• Larger, more complex codebase, making it harder to navigate and understand.
• Wasted effort in maintaining and testing similar code across the project.

How to spot it:

• Look for similar blocks of code across the project or use tools that can identify code duplication.

How to avoid:

• Promote code reuse and modular programming.
• Regular code reviews to identify and eliminate duplication.

How to deal with it:

• Consolidate duplicate code into reusable functions or modules.
• Implement tools to detect and manage code duplication.
• Raise awareness about the importance of avoiding code duplication.

Example:

• Mobile app development: Duplicated code for handling user input across different screens can lead to inconsistencies in user experience.

14. Poorly defined user requirements

This occurs when the requirements from users or stakeholders are not clearly defined or understood, leading to a mismatch between what's developed and what's needed.

Reasons:

• Insufficient dialogue between developers and stakeholders.
• Requirements changing frequently without proper documentation.
• Developers making assumptions about user needs without verification.
• Not spending enough time analyzing and documenting requirements.

Seriousness:

Poorly defined requirements can lead to development efforts that don't meet user needs, resulting in wasted resources and dissatisfaction.

Consequences:

• Development of features that don't align with user needs.
• Need to redo or modify work once the actual requirements are clarified.
• Extended development time due to constant changes and clarifications.
• End products that do not meet user expectations.

How to spot it:

• Frequent changes in development direction.
• Confusion among the development team.
• Dissatisfaction expressed by users.

How to avoid:

• Establish clear channels of communication with stakeholders.
• Implement a rigorous requirement gathering and documentation process.

How to deal with it:

• Keep open communication lines with stakeholders.
• Adopt agile practices to accommodate changing requirements.
• Use prototypes to validate requirements and expectations.

Example:

• Enterprise software development: Developing a feature set without fully understanding user workflows, leading to software that doesn't quite fit the actual business processes.

15. Lack of code reviews

Skipping the process of peer review for written code, which can catch errors, ensure quality, and maintain standards.

Reasons:

• Perceived as time-consuming and resource-intensive.
• A development culture that doesn't value or understand the benefits of code reviews.
• Rush to meet deadlines leading to bypassing code reviews.
• Not having staff with the expertise to conduct effective reviews.

Seriousness:

Without code reviews, the likelihood of bugs, inconsistent coding practices, and poor code quality increases.

Consequences:

• Missing early detection of defects.
• Varied coding styles and practices.
• Reduced knowledge sharing among team members.
• Overall reduction in the quality and maintainability of the code.

How to spot it:

• High incidence of bugs in production.
• Inconsistent coding practices
• Lack of shared understanding of the codebase among team members.

How to avoid:

• Integrate code reviews into the development process.
• Train and incentivize team members for effective code review practices.

How to deal with it:

• Make reviews a required part of the development cycle.
• Implement tools that facilitate code review processes.
• Encourage a culture that values feedback and continuous improvement.

Example:

• SaaS development: Skipping code reviews in a fast-paced SaaS environment can lead to inconsistent features and increased customer-reported issues.

Common tech debt examples in the fintech industry

In the fintech industry, where reliability, security, and compliance are paramount, technical debt can take various forms. Here are some common examples:

Legacy code and systems

‍Outdated systems or code that no longer meets the current standards or requirements. This might include old encryption methods, deprecated libraries, or systems not compatible with new technologies.

Inadequate security measures

‍Fintech companies must adhere to high-security standards. Technical debt can accumulate if the security practices are not updated or if security patches are delayed, leading to potential vulnerabilities.

Poorly structured databases

‍Databases that are not optimized for performance or scalability can become a significant source of technical debt, especially when handling large volumes of financial transactions.

Lack of automated testing

‍Insufficient automated tests for critical financial operations can lead to uncaught bugs, errors, and inefficiencies, increasing the risk in an industry where precision is key.

Code complexity and lack of documentation

‍Overly complex code without adequate documentation can slow down development, making it harder for new team members to understand and modify the codebase.

Non-compliance with regulatory standards

Fintech companies must comply with various regulations like GDPR, PCI-DSS, etc. Technical debt can accumulate if the system is not regularly updated to comply with these changing regulations.

Monolithic architecture

‍A monolithic architecture in a rapidly evolving fintech environment can lead to difficulties in updating and scaling specific parts of the system.

Inefficient third-party integrations

‍Dependence on outdated or inefficient third-party services and APIs can lead to performance bottlenecks and security risks.

Common tech debt examples in the healthcare industry

Outdated legacy systems

‍Continual use of outdated software or hardware that may not integrate well with modern systems. This can lead to inefficiencies and increased risk of errors.

Inadequate security and privacy controls

‍Failure to regularly update security protocols can lead to vulnerabilities, especially critical in healthcare due to the sensitive nature of patient data.

Poorly structured or inconsistent data

‍Data stored in formats that are not standardized or are difficult to access and analyze can hinder effective data use in patient care and research.

Lack of interoperability

‍Systems that cannot communicate effectively with each other can lead to fragmented patient records and inefficiencies in patient care.

Suboptimal Electronic Health Record (EHR) systems

‍EHR systems that are not user-friendly or customized to the specific needs of healthcare providers can lead to inefficiencies and potential patient safety risks.

Non-compliance with regulatory standards

‍Failure to keep systems updated in line with healthcare regulations (like HIPAA in the U.S.) can lead to legal and financial repercussions.

Lack of scalability in IT infrastructure

‍Systems that are not designed to scale with the growing amount of patient data or evolving healthcare practices can become obsolete or inefficient.

Fragmented Patient Engagement Platforms

‍Patient portals or engagement tools that are not integrated or user-friendly can hinder effective communication and care management.

Common tech debt examples in the education industry

Inadequate IT infrastructure

‍Insufficient or outdated IT infrastructure that cannot support the increasing demand for online learning, digital resources, and multimedia content.

Lack of integration between systems

‍Systems and tools used for different purposes (like grading, attendance, and communication) that do not integrate well, leading to inefficiencies and data discrepancies.

Deferred maintenance of educational technology

‍Postponing necessary updates and maintenance of educational technology can lead to inefficiencies and security vulnerabilities.

Lack of scalable solutions for growing student populations

‍IT solutions that are not scalable can struggle to accommodate growing numbers of students or evolving educational needs.

Inadequate cybersecurity measures

Insufficient security measures for protecting sensitive student and staff data, which is increasingly important with the rise of online learning.

Overly complex or user-unfriendly systems

‍Systems that are complex or not user-friendly can hinder their effective use by students, educators, and administrators.

How to avoid tech debt

Fostering a culture of continuous learning

Importance: The tech industry is ever-evolving. New tools, methodologies, and best practices emerge regularly. If teams don't keep up, they risk using outdated methods that can lead to technical debt.

Implementation: Encourage team members to attend workshops, webinars, and conferences. Allocate time for self-learning and exploration. Share knowledge within the team through regular tech talks or knowledge-sharing sessions.

Regular reviews

Importance: Regularly reviewing code, architecture, and processes helps identify potential areas of technical debt before they become significant issues. It ensures that the team is aligned and that the codebase remains maintainable.

Implementation: Conduct code reviews for every piece of code before it's merged. Hold architectural review meetings to discuss and evaluate system design decisions. Periodically review and refine development processes to ensure they're effective.

Proactive planning

Importance: Proactively planning for future requirements can prevent hasty decisions that lead to technical debt. It's about foreseeing potential challenges and preparing for them.

Implementation: Spend time understanding the project's long-term vision and goals. Design systems that are scalable and flexible. Anticipate changes in user requirements or technology landscapes and plan accordingly.

Striking a balance between moving fast and ensuring quality

The Dilemma: Startups and tech companies often operate under the mantra "move fast and break things." While speed is essential, especially in competitive markets, it shouldn't come at the expense of quality. Rushed decisions or shortcuts can lead to significant technical debt.

Finding the Balance: It's essential to prioritize tasks effectively. Not everything needs to be perfect, but critical components, especially those related to security, scalability, and user experience, should not be compromised. Implementing agile methodologies can help teams iterate quickly while maintaining a focus on quality. Feedback loops, both from users and internal teams, can provide insights into where the balance currently stands and where adjustments are needed.

Tech debt: what you can do now

Technical debt, often likened to financial debt, refers to the long-term costs and consequences of shortcuts or suboptimal decisions made during software development. While some technical debt is inevitable, especially in fast-paced environments, it can accumulate and become a significant burden if not addressed. Examples of technical debt include legacy code, insufficient testing, hard-coded values, outdated libraries, and more. Recognizing, managing, and avoiding technical debt requires a blend of continuous learning, regular reviews, proactive planning, and striking a balance between speed and quality.

‍

Your next steps can be:

• Self-Assessment: Review your current projects or systems to identify areas of potential technical debt. Tools like static code analyzers, dependency checkers, and architectural review frameworks can assist in this process.
• Prioritize and Address: Not all technical debt is equal. Prioritize addressing those that have the most significant impact on security, user experience, and future development.
• Educate the Team: Organize workshops or training sessions on best practices, the importance of documentation, and the dangers of accumulating technical debt.
• Implement Regular Reviews: Schedule regular code and architectural reviews. Consider adopting methodologies like Agile or Scrum that emphasize iterative development and continuous feedback.
• Stay Updated: Keep abreast of the latest in software development practices, tools, and methodologies. Encourage a culture of continuous learning within the team.
• Seek External Input: Sometimes, an external perspective can provide fresh insights. Consider periodic audits or consultations with external experts to get an unbiased view of your technical debt situation.‍
• Iterate and Improve: Addressing technical debt is not a one-time activity. Continuously monitor, address, and learn from past decisions to ensure a sustainable and efficient development process.