February 17, 2026

What is Technical Debt and How to Fix it

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In modern software development, quality and continuous delivery have become the core priorities for staying competitive. Teams are under pressure to release new features rapidly and meet tight deadlines. Even though these practices can help in achieving short-term goals, they can also lead to code quality compromises and structural issues, which silently weaken the system and make future developments slower and riskier.
Ignoring technical debt management can significantly reduce development speed, increase defect rates, and limit a system’s scalability. It is, therefore, essential to understand technical debt in order to build sustainable and strong software systems. In this article, we will dig deep into technical debt, its causes and warning signs, technical debt types, and how to identify, measure, and reduce it.

What is Technical Debt?

Technical debt is the accumulation of decisions (code, design, process, etc.) to achieve short-term speed that later leads to an increase in effort, cost, or risks of maintaining and scaling a software system. It is the long-term cost of opting for a shortcut to achieve speed instead of sustainable and well-designed code. This increase of effort or cost becomes the consequential technical debt, also known as code debt or design debt.

Ward Cunningham, an expert software developer, first used the term ‘Technical Debt’ to explain the concept to non-technical stakeholders. He compared it to financial debt, as it also has to be paid in the future with interest. According to Steve McConnell, there are two major technical debt types: Intentional Technical Debt and Unintentional Technical Debt.
Intentional Technical Debt: It refers to deliberate and conscious technical compromises to achieve faster goals, such as faster delivery, early user feedback, or market advantage. The team knowingly postpones refactoring code to prioritize delivery with the understanding that the solution is temporary and will require improvement later. The reasons may vary from tight deadlines and limited resources to competitive pressure to deliver fast.
Unintentional Technical Debt: Unintentional or unconscious debt results from poor planning and a lack of knowledge or experience. It happens when the team follows suboptimal technical practices with no foresight of future problems. These risks are only recognized later when tracing back becomes more difficult.

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Forms of Technical Debt

Technical debt is not just about code complexities, but rather comes in different shapes. Understanding these forms is crucial because each type impacts the software system differently, influencing maintainability, scalability, and long-term efficiency. We’ve shared some prominent technical debt types below.

Type of Technical Debt	What Does it Mean?
Code Debt	Poor or messy code, which reduces readability and maintainability.
Design Debt	UI/UX flaws that affect usability and user experience.
People Debt	Skills gaps or insufficient team training result in an inefficient system.
Build Debt	Issues that slow down or complicate software builds.
Infrastructure Debt	Refers to problems with servers, databases, or underlying systems.
Architecture Debt	Core structural problems that make future changes difficult.
Test Debt	Excessive effort is spent on maintaining tests instead of creating new ones.
Requirement Debt	Gaps between what the system should do and what it actually does.
Documentation Debt	Missing or outdated documentation that hinders knowledge transfer.
Defect Debt	Bugs that are known, but keep getting postponed due to other priorities.
Service Debt	Incompatible or poorly designed web services or APIs.
Process Debt	Inefficient processes that slow down development or increase errors.
Test Automation Debt	Delayed automation of tests for continuous integration and faster releases.

Causes of Technical Debt

Technical debt arises when decisions, constraints, or conditions during software development create a gap between the current implementation and an ideally planned solution. Some of the causes of technical debt are discussed below:

The pressure to release products on tight deadlines often leads to teams prioritizing tech debts and accepting long-term software maintenance costs for short-term gains.
Limited technical knowledge or experience may result in poor design or low scores on code quality metrics.
Weak planning for maintainable software architecture can cause scalability issues as the system grows.
Consciously postponing code improvement and refactoring strategy to prioritize new features leads to intentional technical debt.
Other factors, like dependence on outdated technologies and communication gaps, result in accidental design flaws, which increase future maintenance complexities.

Signs of Technical Debt

Technical debt doesn’t become a problem overnight, and it is rather a gradual degradation of the system. There are several red flags that your development team can identify for eliminating technical debt. You might need to work on technical debt management, if:

Making simple changes or implementing updates takes too long.
The frequency of bugs increases even after small changes.
Code becomes difficult to understand because of a lack of documentation or poor structure.
Developers hesitate to modify specific parts of the codebase due to the fear of system breakdown.
Testing often becomes slow and unreliable, and development efficiency suffers, which reduces the team’s confidence in releasing new features.
Refactoring is repeatedly postponed while issues continue to arise.

Methods and Tools to Identify and Measure Debt

Apart from the above signs, there are different ways and tools to help you identify and measure tech debt. Some of these methods are:

1. Developer Input

A developer’s firsthand experience can never go wrong in identifying technical debt. They can pinpoint the difficult, fragile, and risky parts of the system and the size and impact of the debt by calculating the estimated effort required for refactoring. It provides a practical, effort-based view of technical debt that may be hard to capture through automated metrics alone.

2. Stakeholder Feedback

Stakeholders don’t highlight technical debt directly, but through their concerns about slow delivery, reduced feature velocity, or system inability. The team must take repeated delays or increased costs into consideration, as they may indicate underlying technical debt.

3. Code Reviews

Code review is a promising way to identify violations of coding standards, poor design choices, and unnecessary complexities. Repeated complaints on the same issue are a sign of accumulated debt.

4. Defect and Bug Tracking

Tracking bugs and defect rates helps identify different technical debt types. While recurring bugs indicate unresolved structural or design issues, high defect rates in certain components suggest concentrated technical debt. The time spent on bug resolution measures how technical debt affects maintenance effort.

5. Static Analysis Tools

When choosing automation tools, the development team should also look into static analysis tools, which can automatically detect code smells, complexities, and rule violations. Modern static analysis platforms such as SonarQube, Code Climate, and Codacy provide quantifiable insights into code quality by assigning maintainability ratings and estimating the effort required to remediate identified issues.
Furthermore, tools like NDepend (for .NET environments) and ESLint or Checkstyle (for language-specific enforcement) further support teams by highlighting dependency complexity, architectural weaknesses, and non-compliant coding patterns. These insights allow teams to prioritize refactoring efforts based on risk and impact rather than intuition.

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How to Fix or Reduce Technical Debt?

1. Identify Weak Areas and Quantify Debt

The first step towards technical debt management and eliminating technical debt is to find out the weak spots. Development teams can systematically identify weak areas in the codebase through different approaches like code reviews, static analysis tools, and architectural assessments. Once the targets are in sight, the next step involves quantifying and calculating technical debt. Developers can use code quality metrics such as code complexity, duplication, or defect density to help them understand the scale of the problem and make informed improvement decisions.

2. Planned Time and Backlogs

Once the team has identified the critical areas that require urgent attention, the right approach is to work on them as a first priority and not as an optional task. An effective way is to assign expert developers and allocate dedicated time for dealing with technical debt reduction within scheduled plans and release cycles. This ensures that maintenance work is treated as a regular responsibility and is given the continual attention it requires. Moreover, managing technical debt items in the product backlog improves visibility and prevents them from being repeatedly compromised in order to prioritize working on new features.

3. Business-Driven Debt Prioritization

It is essential to find out which debt is affecting the critical components of the business, specifically revenue generation and product releases. Once the team identifies these debts, they can focus their attention and effort on the debts that directly affect performance, security, customer experience, and delivery speed. As a result, development team efforts remain aligned with organizational goals while reducing long-term costs and maximizing return on management investment.

4. Refactoring Strategy and Monitoring Progress

Focusing on all issues at once can go wrong in many ways. It is better for the development teams to follow refactoring strategies that address debt in small and manageable steps. On top of that, the team should consider refactoring code alongside new changes and updates to reduce the risks of future technical debt.
In addition, constant progress monitoring, trend analysis, and code health assessment (using code quality metrics) allow teams to measure improvement and control risks. It also ensures that the refactoring strategy is consistently working in the right direction and is producing measurable long-term benefits.

5. Automate Testing and Integration for Higher Efficiency

Testing and integration can take a lot of time and effort when done manually. In contrast, automated unit tests, integration tests, and continuous integration pipelines are less time-consuming, more efficient, and help prevent new technical debt from being introduced during development.
There are various testing tools like Jenkins, Bamboo, Selenium, PlayWright, and Cypress that can play a role in reducing and eliminating technical debt. Higher efficiency in testing provides confidence during refactoring and ensures that changes do not introduce new debts, and thus stabilizes the system as debt is reduced.

6. Documentation and Proper Team Training

Maintaining clear documentation and investing in regular team training helps prevent knowledge gaps that often lead to accidental/unintentional technical debt; well-trained developers are more likely to follow best practices, understand system architecture better, and make design decisions that promote maintainable software architecture.
With all that, regular team meetings and collaborative discussions are a must to ensure that knowledge is shared consistently. All three practices together reduce the risks of misunderstandings and work on aligning everyone on best practices.

7. Future-Oriented System Design

In order to follow the changing trends and not fall behind, the most effective strategy is to design software with scalability and flexibility in mind. This reduces the likelihood of future technical debt. Practices such as clean architecture, loose coupling, clear interface, and integration of containerization technology in your system allow systems to evolve without requiring extensive coding and rework. When reinforced with automated testing and continuous integration, these practices ensure that changes can be introduced safely, consistently, and with measurable impact on system stability.

8. AI-Enabled Development Workflows

AI-powered tools are here to make things easier for developers, so why not use them for the best interests? Developers can use AI-powered tools to detect code smells to tackle the issue before it gets too complicated. Not only that, AI tools can suggest refactoring opportunities, automate code reviews, and improve test generation. Integrating AI into the system will help automate workflows, increase development efficiency, identify technical debt early, and eventually reduce long-term costs.

Final Words

Technical debt is an unavoidable aspect of modern software development, which often arises from the need to deliver quickly, even at the cost of long-term code quality and system design. Ignoring technical debt managementmay become the underlying reason for more maintenance effort, slow development, and higher risks to the system.

By understanding the nature of technical debt, its causes, types, and warning signs, development teams can make informed decisions to control it in a timely and effective manner. Regular practices, like code quality assessment, business-driven prioritization, refactoring, automated testing, future-oriented design, and using AI-enabled tools, will allow teams to achieve delivery goals whilereducing long-term costs.

We, at Deliverydevs, understand the intricacies of technical debt identification and management, ensuring that your codebase is continuously assessed, risks are proactively addressed, and future development is not constrained by hidden complexity.

Ready to be on the top of your game with efficient software systems? Get in touch with us today!

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