Technical Debt

What is Technical Debt?

Technical debt is the additional cost and effort required to make changes in a software system, incurred when teams opt for expedient, short-term solutions instead of implementing more robust or maintainable approaches. The term draws an analogy to financial debt: quick solutions deliver immediate value but accrue “interest” over time in the form of increased maintenance, complexity, and future remediation costs.

Ward Cunningham, who coined the term, explained it as the cost that comes from “going faster now at the expense of having to do more work later.” Technical debt is not limited to code; it encompasses all aspects of software systems, including architecture, documentation, infrastructure, testing, and processes.

Example: Hardcoding a database connection string allows for rapid deployment but means future changes require manual edits in multiple locations, increasing risk and maintenance overhead.

Technical debt can be strategic when consciously managed for business priorities, but becomes problematic when allowed to accumulate without visibility or remediation.

Why Does Technical Debt Occur?

Tight Deadlines: Teams prioritize delivery speed, taking shortcuts or omitting refactoring

Changing Requirements: Scope changes can render previous solutions suboptimal, requiring workarounds or patches

Skill Gaps: Developers unfamiliar with best practices may introduce inefficient, unscalable, or error-prone code

Resource Constraints: Limited budgets, personnel, or time force trade-offs on quality and sustainability

Poor Communication: Misalignment among stakeholders leads to misunderstandings, rework, and duplicated efforts

Legacy Systems: Maintaining older systems often means layering new functionality onto outdated foundations

Lack of Documentation: Inadequate documentation increases onboarding time and the risk of accidental errors

Real-World Example: A startup rapidly develops a minimum viable product (MVP) to enter the market. To save time, it skips automated testing and code reviews. As the codebase grows, the lack of testing infrastructure leads to recurring bugs and slows the addition of new features—a textbook case of technical debt spiraling.

Types of Technical Debt

By Intent and Awareness (Martin Fowler’s Technical Debt Quadrant)

Deliberate Debt: Intentionally incurred to meet business goals, with a plan for remediation

Accidental (Inadvertent) Debt: Results from mistakes, lack of experience, or unforeseen consequences

Reckless Debt: Shortcuts taken with disregard for quality or future impact

Prudent Debt: Well-understood trade-offs, consciously managed

By Origin or Domain

Architecture Debt: Flaws in system structure that hinder scalability, maintainability, or flexibility (e.g., a tightly coupled monolithic system instead of modular services)

Code Debt: Poor coding practices, inconsistent style, duplicated logic, or lack of adherence to standards (e.g., repeated code blocks instead of reusable functions)

Design Debt: Violations of design principles, such as improper inheritance or lack of encapsulation

Defect Debt: Known bugs or issues deferred for future resolution

Documentation Debt: Missing, outdated, or insufficient technical documentation

Build Debt: Inefficient, unreliable, or manual build and deployment processes

Infrastructure Debt: Outdated servers, scripts, or configurations

Process Debt: Ineffective workflows, absence of automation, or unclear processes

People Debt: Insufficient training, knowledge silos, or poor onboarding

Requirement Debt: Incomplete, poorly defined, or partially implemented requirements

Security Debt: Ignored or delayed security best practices and vulnerability patches

Test Debt: Lack of automated or comprehensive tests

Test Automation Debt: Manual tests that should be automated for scalability and reliability

Data Debt: Poor data models, legacy schemas, or lack of data governance

How is Technical Debt Used? (Context and Use Cases)

Software Development: Debt items are tracked alongside feature development and bug fixes using tools like Jira

Project Management: Product owners weigh the cost/benefit of debt remediation versus new features during planning

DevOps and Infrastructure: Teams automate, refactor, and update deployment pipelines to minimize future debt

AI Infrastructure & Deployment: Machine learning systems require special attention due to their susceptibility to hidden technical debt

Example Use Cases

Sprint Planning: Allocating sprint capacity (e.g., 20%) for refactoring and addressing debt

Security Audits: Logging and prioritizing outdated libraries and vulnerabilities as security debt

Onboarding: Reducing documentation debt to accelerate new developer ramp-up

AI Workloads: Investing in modularizing and automating data pipelines to address infrastructure and process debt

Impacts of Technical Debt

Reduced Development Speed: Feature addition slows as codebase complexity grows

Increased Maintenance Costs: More resources spent on fixing issues and workarounds instead of innovation

Lower Software Quality: Accumulated shortcuts lead to more bugs and reliability issues

Poor Scalability: Systems become harder to extend or adapt to changing needs

Security Vulnerabilities: Outdated components and ignored controls increase exposure to attacks

Resource Drain: Up to 20-33% of IT budgets and engineering time are consumed by technical debt

Business Risks: Delays, missed opportunities, compliance failures, and reputational harm

Team Morale: Repeated firefighting leads to burnout and turnover

How to Identify Technical Debt

Code Reviews: Uncover shortcuts, complexity, and missing tests

Automated Code Analysis: Tools like SonarQube and CodeClimate flag code smells, duplication, and complexity

Developer Feedback: Recurring complaints about certain modules signal debt hotspots

Issue Tracking: Frequent bug reports in the same areas

Performance Monitoring: Identifying resource spikes or degraded response times

User Feedback: Reports of poor performance or reliability may point to hidden debt

How to Measure Technical Debt

Technical Debt Ratio (TDR)

Ratio of remediation cost to development cost:

• TDR = (Cost to fix codebase) / (Cost to build codebase)

SQALE Method

Framework for estimating remediation costs and business impact, expressing debt in developer hours or financial terms.

Gartner Method

Rates debt items by risk, business impact, likelihood, and remediation cost.

Additional Metrics

Code Complexity: High cyclomatic complexity or coupling signals debt

Bug Resolution Time: Longer fix times in certain modules

Legacy Code Percentage: Ratio of untested or unsupported code

Open Debt Items: Number and severity of logged debt tasks

How to Manage and Reduce Technical Debt

1. Acknowledge and Define Debt
Ensure all stakeholders understand what constitutes technical debt.

2. Make Debt Visible
Track debt alongside regular development work.

3. Prioritize Remediation
Use risk and cost frameworks (e.g., SQALE, Gartner) to prioritize.

4. Integrate Remediation
Allocate dedicated sprint capacity for debt reduction.

5. Automate Testing and Validation
Implement CI/CD pipelines and automated tests.

6. Refactor Incrementally
Break down large items and focus on high-impact areas.

7. Educate Teams
Train developers and product owners on long-term costs and risks.

8. Monitor Progress
Track and report on debt reduction.

9. Prevent New Debt
Enforce standards, peer reviews, and automation.

Examples of Technical Debt in Practice

Hardcoded Values: Credentials embedded in code, complicating environment changes

Skipped Error Handling: Rushed releases lack robust error handling, causing crashes

Tightly Coupled Components: Interdependent legacy features hinder isolated updates

Outdated Dependencies: Unpatched libraries introduce security and maintenance risks

Manual Deployment: Scripts require manual intervention, slowing delivery and increasing error rates

Technical Debt in AI Infrastructure & Deployment

Machine learning and AI systems are especially prone to hidden technical debt due to rapid prototyping and experimental development.

Boundary Erosion: ML models erode modularity, making it hard to maintain strict abstraction boundaries

Entanglement: Model features and data dependencies become tightly coupled, so “Changing Anything Changes Everything” (CACE principle)

Correction Cascades: Stacking models or correction layers increases system complexity and interdependence

Undeclared Consumers: Downstream systems silently depend on ML output, increasing maintenance risk

Data Dependencies: Data pipeline or training data changes can silently break models

Configuration and External Drift: Model behavior can change if the external world shifts or configuration is inconsistent

Specific AI/ML Examples

• Prototype research code is deployed into production without modularization or testing
• Data pipelines are manual or brittle, leading to inconsistent results and reproducibility issues
• Lack of model and data versioning complicates updates and auditing
• Models run on outdated infrastructure, hampering scalability and maintenance

Tools and Templates for Managing Technical Debt

Jira: Track and prioritize debt alongside features and bugs

Ardoq: Visualize and quantify debt across portfolios

SonarQube: Automated code quality and debt analysis

SQALE Method: Framework for debt quantification and reporting

AWS Transform Custom: Automate code and infrastructure modernization at scale

Frequently Asked Questions (FAQ)

What is the difference between technical debt and bugs?
Technical debt is the cumulative effect of shortcuts or compromises, while bugs are defects causing incorrect behavior. Some bugs are caused by technical debt, but not all debt is a bug.

Is technical debt always bad?
No. Strategic technical debt enables business agility when managed and remediated before costs spiral.

Who is responsible for technical debt?
Responsibility is shared across developers, managers, and business stakeholders. Developers often identify and remediate, while product owners prioritize.

How can technical debt be prevented?
Set realistic deadlines, enforce code reviews, automate testing, and foster a culture valuing long-term code health.

How do you measure technical debt?
Combine qualitative (reviews, feedback) and quantitative (metrics, SQALE, TDR) methods, tracking open items, remediation cost, and business risk.

Can AI help manage technical debt?
Yes. AI tools can identify code smells, automate code modernization, and suggest refactoring, but always require human oversight.

What are “code smells” in the context of technical debt?
Symptoms in source code (e.g., duplicate code, long methods, excessive coupling) indicating deeper problems and often leading to technical debt.

Key Takeaways

• Technical debt reflects the cost of future work created by expedient decisions
• It arises from both deliberate trade-offs and accidental mistakes
• Debt can be classified by type (architecture, code, process, security) and intent (deliberate/prudent vs. reckless/accidental)
• Unmanaged debt slows development, increases risk, and drains resources
• Identification and measurement rely on code reviews, metrics, and specialized tools
• Management requires visibility, prioritization, integration into workflows, and cultural alignment
• AI/ML systems are especially susceptible due to rapid prototyping and complex dependencies
• Use project management, code quality, and automation tools to quantify and control technical debt

References

• Atlassian: What is Tech Debt?
• IBM: What is Technical Debt?
• Ardoq: An Introduction to Tech Debt
• Martin Fowler: Technical Debt Quadrant
• Ward Cunningham Explains Debt Metaphor
• SIG: Five Types of Technical Debt
• NeurIPS: Hidden Technical Debt in Machine Learning Systems (PDF)
• SQALE Technical Debt Framework (PDF)
• AWS Transform Custom for Code Modernization
• Wikipedia: Technical Debt
• ProductPlan: Technical Debt
• Mendix: What is Technical Debt?
• Forbes: Measuring and Managing Technical Debt
• AWS Blog: Introducing AWS Transform Custom
• CircleCI: Manage and Measure Technical Debt
• OpsLevel: How to Measure Technical Debt
• vFunction: How to Measure Technical Debt
• ResearchGate: Towards an Ontology of Terms on Technical Debt
• Atlassian: Jira Templates
• Atlassian: Jira Software
• SonarQube
• CodeClimate
• SQALE Method
• Atlassian: Agile Software Development
• Atlassian: Agile Project Management
• IBM Think: Artificial Intelligence
• IBM Think: DevOps