What is technical dept and how to manage it
Introduction
Technical debt refers to the trade-offs made in software development when prioritizing speed and short-term gains over long-term code quality and maintainability.
It occurs when developers implement quick fixes, workarounds, or suboptimal solutions that will require additional effort to refactor or improve in the future.
Much like financial debt, technical debt “accumulates interest” over time—meaning that the longer it remains unaddressed, the more costly and difficult it becomes to fix.
While taking on technical debt can sometimes be a strategic decision to meet tight deadlines or release a minimum viable product (MVP) quickly, failing to manage it properly can lead to slower development, increased bugs, higher maintenance costs, and frustrated teams.
Technical debt can arise from poor design decisions, outdated technology, lack of documentation, or rushed development cycles.
To maintain software quality and long-term sustainability, teams must regularly assess, prioritize, and pay down technical debt before it hinders progress.
What is Technical Debt?
The Messy Room Analogy
Imagine you’re moving into a new house. You’re in a hurry to unpack, so instead of organizing everything properly, you just dump boxes in random rooms and shove things into closets.
At first, this works fine—you’re settled in quickly, and everything seems okay. But over time, finding things becomes harder, clutter piles up, and cleaning takes much longer. Eventually, you’ll have to spend a lot of time reorganizing everything, and the longer you wait, the worse it gets.
Technical debt works the same way. Rushed coding decisions may speed up development in the short term, but they create a messy, hard-to-maintain codebase that slows down future work. The more debt you accumulate, the harder it is to “clean up” later.
Business Pressures & Poor Planning – Technical debt often arises from the urgency to release products faster, last-minute changes in project specifications, and inadequate documentation or testing of modifications.
Gaps in Knowledge & Expertise
A lack of process understanding, insufficient technical leadership, poor mentoring, and ineffective knowledge-sharing practices can lead to inefficient development decisions and accumulating technical debt.
Development Process Inefficiencies
Issues such as implementing suboptimal solutions, unclear or incomplete requirements, conflicting changes in parallel development branches, postponed refactoring, and delays in integrating upstream contributions can contribute to technical debt.
Failure to Follow Best Practices
Factors such as inadequate software documentation, weak collaboration, lack of ownership, frequent rewrites (especially for outsourced projects), insufficient focus on code quality, tightly coupled components, absence of a proper test suite, and disregard for industry standards and frameworks all contribute to long-term technical debt.
Signs of Technical Debt
Slow development speed.
Frequent bugs and crashes.
High onboarding time for new developers.
Code complexity and lack of documentation.
The Impact of Technical Debt
Higher maintenance costs.
Slower feature development.
Increased risk of bugs and security vulnerabilities.
Developer frustration and burnout.
Customer frustration
How to Manage and Reduce Technical Debt
Identify and Track Debt
Regularly review code quality.
Use static analysis tools to detect issues.
Static analysis tools:
SonarQube
Best for: Identifying code smells, bugs, security vulnerabilities, and technical debt
Key Features:
Supports multiple programming languages
Provides a technical debt estimation feature
Offers code quality gates and continuous integration (CI) integration
Detects security vulnerabilities (OWASP, SANS, etc.)
Use Case: Ideal for enterprise teams and DevOps pipelines looking for an all-in-one static analysis tool.
ESLint (For JavaScript & TypeScript)
Best for: Detecting code issues in JavaScript and TypeScript projects
Key Features:
Helps enforce coding standards and best practices
Identifies unused variables, improper syntax, and performance issues
Highly customizable with plugins and rule configurations
Use Case: Essential for front-end and Node.js developers to maintain clean and efficient JavaScript code.
Pylint (For Python)
Best for: Python code quality and linting
Key Features:
Detects errors and enforces PEP 8 style guidelines
Flags unused imports, duplicate code, and bad practices
Assigns a score to the code based on quality and maintainability
Use Case: Perfect for Python developers aiming to reduce technical debt and maintain clean, readable code.
Checkstyle (for Java)
Best for:
Enforcing Java coding standards and detecting style violations
Key Features:
Finds class design issues, unused code, and missing documentation
Helps teams maintain consistent code formatting
Integrates well with Maven and Gradle
Use Case: Ideal for Java teams working on large codebases that need structured and maintainable code.
CodeClimate
Best for: Automated code quality reporting and technical debt analysis
Key Features:
Provides technical debt estimation with a maintainability score.
Supports multiple languages like Python, Ruby, Java, and JavaScript.
Integrates with GitHub, GitLab, and CI/CD pipelines.
Use Case: Great for teams that need detailed insights on code quality and maintainability across multiple languages.
Final Thoughts
If you’re looking for a general all-in-one tool, SonarQube or CodeClimate would be your best choice. However, for language-specific needs, ESLint, Pylint, and Checkstyle provide deeper analysis.
Prioritize Fixing Debt
Categorize technical debt based on severity.
Address critical areas first (security, performance).
Implement Best Practices
Follow clean code principles.
Write automated tests.
Encourage proper documentation.
Comments should explain why the code exists, not just what it does.
Good Example (Python)
Convert temperature from Fahrenheit to Celsius using standard formula
def fahrenheit_to_celsius(fahrenheit):
return (fahrenheit - 32) * 5 / 9Bad Example (Too obvious)
Function to subtract 32, multiply by 5, and divide by 9
def fahrenheit_to_celsius(fahrenheit):
return (fahrenheit - 32) * 5 / 9Use Docstrings for Functions, Classes, and Modules
A docstring is a structured comment at the start of a function, module, or class that describes its purpose, parameters, and return value.
Example (Python – Docstring Format)
def add_numbers(a: int, b: int) -> int:
“””
Adds two numbers together.
Args:
a (int): The first number.
b (int): The second number.
Returns:
int: The sum of both numbers.
"""
return a + b
Tip: Use a consistent docstring format like Google, NumPy, or reStructuredText.
Maintain a README File
A README.md file is essential for any project to provide high-level information.
What to Include in a README?
Project Overview – What the project does
Installation Steps – How to set up the project
Usage Instructions – How to run/test the code
Contributing Guide – Guidelines for new developers
License – The project’s licensing information
Example Markdown
My Awesome Project ????
Overview
This project converts text into different formats and provides an API.
Installation
“`sh
git clone https://github.com/user/repo.git
cd repo
pip install -r requirements.txt
Usage
Python:
from converter import text_to_uppercase
print(text_to_uppercase(“hello”)) # Outputs: HELLO
Yaml:
##**Use Inline Comments Sparingly**
Use inline comments only when *necessary* to explain complex logic.
###Example (JavaScript)
“`js
// Cache the user data to prevent multiple API requests
const userCache = new Map();
Tip: Don’t overuse inline comments; clean and readable code should mostly speak for itself.
Keep API Documentation Updated
If you’re developing an API, use tools like Swagger or Postman to document endpoint
Example (Swagger YAML format):
paths:
/users/{id}:
get:
summary: Get user details
parameters:
– name: id
n: path
required: true
schema:
type: integer
responses:
200:
description: User details retrieved successfully
404:
description: User not found
ip: Keeping API documentation auto-generated using tools like OpenAPI ensures it stays up to date.
Follow a Consistent Naming Convention
Annotations help document code behavior, especially in frameworks.
Example (Java - Spring Boot):
Java:
@RestController
@RequestMapping("/users")
public class UserController {
@GetMapping("/{id}")
public ResponseEntity<User> getUser(@PathVariable Long id) {
return ResponseEntity.ok(userService.getUserById(id));
}
}
Tip: Annotations reduce the need for excessive comments and improve code clarity.
Auto-Generate Documentation with Tools
Instead of writing everything manually, use documentation tools that generate API docs from comments.
Popular Tools
Example (JSDoc – JavaScript):
/**
* Calculates the area of a rectangle.
* @param {number} width - The width of the rectangle.
* @param {number} height - The height of the rectangle.
* @returns {number} The calculated area.
*/
function calculateArea(width, height) {
return width * height;
}
Tip: Automating documentation saves time and ensures consistency.Version Your Documentation
If your software evolves, older versions of the documentation should still be available for reference.
Tip: Tools like Docusaurus or GitBook help maintain versioned documentation.
Keep Documentation Up to Date
Set a “Documentation Review” schedule to ensure it stays relevant.
Outdated documentation can be worse than none at all!
Final Thoughts
Write meaningful comments (but avoid unnecessary ones).
Use docstrings for functions & classes for clarity.
Maintain a structured README file for project-level documentation.
Utilize tools to automate documentation where possible.
Regularly update documentation to prevent outdated or misleading information.
Define Clear Goals and Requirements
Prevents scope creep and ensures alignment with business objectives.
Best Practices:
Clearly document the problem statement and expected outcomes.
Work closely with stakeholders to gather detailed requirements.
Prioritize features using MoSCoW (Must-have, Should-have, Could-have, Won’t-have) method.
Avoid vague or ambiguous requirements.
Example: Instead of “Improve app performance,” define: “Reduce page load time to under 2 seconds.”
Break Down Work into Manageable Tasks
Makes progress trackable and helps avoid overwhelming the team.
Best practices
Use the Agile methodology to divide work into sprints.
Break tasks into small, independent, and testable units (e.g., user stories or tickets).
Assign estimated effort using techniques like Story Points or T-shirt sizing (S/M/L).
Example: Instead of “Implement user authentication”, break it down into:
Create a login page UI.
Implement OAuth authentication.
Add password reset functionality.
Choose the Right Tech Stack
Helps prevent future migration issues and compatibility problems.
Best Practices:
Select technologies based on scalability, maintainability, and community support.
Consider long-term support and security of frameworks/libraries.
Avoid unnecessary complexity (e.g., using microservices when a monolith is sufficient).
Example: Choose React.js for front-end if you need component-based UI with a strong ecosystem.
Allocate Resources Wisely
Prevents burnout and ensures steady progress.
Best Practices:
Assign tasks based on team expertise and availability.
Ensure a balance between feature development, testing, and documentation.
Plan for buffer time to accommodate unexpected issues.
Example: If a project is estimated to take 4 months, allocate an extra 2 weeks for debugging and testing.
Establish Clear Development Workflows
Improves efficiency and minimizes miscommunication.
Best Practices:
Use a Git workflow (e.g., GitFlow, trunk-based development).
Define coding standards and enforce code reviews.
Automate CI/CD pipelines for continuous testing and deployment.
Example:
GitFlow Workflow
feature/branch-name → for new features
bugfix/branch-name → for bug fixes
release/branch-name → for preparing releases
hotfix/branch-name → for urgent fixes
Plan for Testing and Quality Assurance (QA)
Reduces post-release bugs and improves reliability.
Best Practices:
Follow Test-Driven Development (TDD) or Behavior-Driven Development (BDD).
Use unit tests, integration tests, and end-to-end (E2E) tests.
Automate tests where possible (e.g., Selenium, Jest, Cypress).
Example: A rule like “Every feature must have at least 80% test coverage before merging.”
Manage Risks Proactively
Helps avoid unexpected setbacks.
Best Practices:
Identify potential risks early (e.g., scope changes, dependency failures, security concerns).
Maintain a risk register with mitigation strategies.
Have a backup plan for critical dependencies (e.g., alternative APIs).
Example: If you rely on a third-party API, plan for rate limits, downtime, and alternative services.
Balance Speed vs. Technical Debt
Why? Short-term shortcuts can lead to long-term problems.
Best Practices:
Allocate time for refactoring and paying off technical debt.
Use tools like SonarQube to monitor code quality.
Encourage writing clean, maintainable code rather than quick fixes.
Example: Instead of rushing a feature for a deadline, allocate one extra sprint to refine and optimize it.
Set Realistic Deadlines and Milestones
Prevents burnout and keeps the project on track.
Best Practices:
Use SMART Goals (Specific, Measurable, Achievable, Relevant, Time-bound).
Set smaller milestones instead of a single big deadline.
Regularly review progress in standups or sprint reviews.
Example: Instead of “Launch in 3 months,” define:
Month 1: Complete backend API.
Month 2: Develop front-end UI.
Month 3: Test, refine, and deploy.
Document Everything
Helps future developers understand the project.
Best Practices:
Maintain a README.md with project setup and guidelines.
Use docstrings for functions, APIs, and complex logic.
Keep an Architecture Decision Record (ADR) for major changes.
Example: Instead of undocumented APIs, generate OpenAPI/Swagger docs for easy reference.
Final Thoughts
Plan thoroughly but stay adaptable –Expect changes and handle them efficiently.
Break work into small, testable pieces – Makes development manageable.
Balance speed with quality – Avoid excessive technical debt.
Use automation and best practices – Improves consistency and reduces errors.
Communicate and document clearly – Avoid misunderstandings in teams.
Invest in refactoring periodically.
Balance speed with quality in project management.
Case Studies & Real-World Examples
Sonos’s Software Challenges
In early 2025, Sonos released a smartphone application intended to enhance user experience by integrating seamlessly with their wireless speakers. However, the app was plagued with bugs that severely impacted product functionality. Users reported frequent disconnections, unresponsive controls, and difficulties in syncing multiple speakers. These issues not only frustrated customers but also led to a substantial financial setback, with the company reporting a $100 million revenue loss. The severity of the situation culminated in the resignation of the CEO.
ft.com
Analysis
This incident underscores the critical importance of rigorous software development practices and comprehensive testing before deployment. The rush to release the new app likely led to inadequate testing and quality assurance, resulting in technical debt that manifested as severe bugs and user dissatisfaction. The financial and reputational damage experienced by Sonos highlights how technical debt, if not properly managed, can escalate into significant business challenges.
Lessons Learned
Prioritize Quality Assurance: Implement thorough testing protocols to identify and rectify bugs before release.
Manage Technical Debt: Regularly assess and address technical debt to prevent accumulation that can lead to larger issues.
Customer Communication: Maintain transparent communication with users during software rollouts to manage expectations and promptly address concerns.
By learning from Sonos’s experience, software companies can better navigate the complexities of technical debt and its potential impact on both products and business operations.
Conclusion
Technical Debt is Inevitable but Manageable – While technical debt is a natural part of software development, failing to address it can lead to long-term problems such as high maintenance costs, slow development, and increased security risks.
Proactive Management is Key – Regularly identifying, tracking, and prioritizing technical debt helps prevent it from becoming overwhelming. Using static analysis tools and categorizing debt by severity ensures that the most critical issues are addressed first.
Following Best Practices Reduces Debt Accumulation – Implementing clean coding principles, maintaining proper documentation, writing automated tests, and investing in refactoring can significantly reduce the impact of technical debt over time.
Balancing Speed and Quality is Essential – While delivering features quickly is important for business goals, prioritizing short-term gains over maintainability can create long-term challenges. Finding the right balance between speed and sustainability ensures long-term software health.
A Culture of Continuous Improvement is Necessary – Encouraging collaboration, code reviews, and knowledge sharing within development teams helps prevent unnecessary technical debt and promotes a sustainable coding environment.
Final Thought
Managing technical debt is an ongoing process that requires consistent effort and strategic planning. By taking proactive steps, teams can ensure that their software remains scalable, maintainable, and cost-effective in the long run.
