A Practical Guide to Reducing Technical Debt

Unmanaged technical debt can inflate project costs by 10% to 20% and cause budget and schedule overruns of up to 66%.

Developers waste on average 23% of their working time due to technical debt, which is over a day a week in lost productivity.

One of our clients had a 1.7-year backlog of features and functionalities and accumulated technical debt. Technical debt had become a roadblock, as their internal resources were constantly being pulled in to resolve important technical and functional issues.

Every hour spent untangling messy code is an hour not spent building features that serve your customers.

The benefits of reducing technical debt extend beyond cleaner code. Teams ship faster, onboarding takes days instead of weeks, and production incidents decrease. And now, the next or current big thing – AI automation – requires, in many cases, an underlying application code base that is updated and stable.   But getting there requires strategy, the right tools, and often the right partner to execute effectively.

 

Understanding Technical Debt in Agile and Scrum Environments

What Is Technical Debt? 

Ward Cunningham coined the term technical debt in 1992 to explain a simple idea: every shortcut you take in software development creates a bill that you’ll eventually pay later. 

Based on that, technical debt refers to future costs from relying on shortcuts or suboptimal decisions made during initial development. 

You choose speed, but you’ll spend extra time fixing, maintaining, and working around those decisions down the road.

This metaphor works because technical debt literally accumulates interest over time. Most software products carry some degree of technical debt, and that’s normal. The problem, however, starts when teams ignore it or let it pile up without a plan to address it. 

In other words, technical debt and agile development intersect constantly; if the teams prioritize working software and quick iterations, they’ll be debt-free. 

Technical Debt: How Agile Teams Accumulate Tech Debt

Agile environments create technical debt faster than traditional development approaches. Teams are pushed to prioritize delivery over clean code.

Does this scenario seem familiar?  

Your team commits to a two-week sprint, stakeholders want specific features, and you’re choosing between shipping on time or refactoring that messy module.

In the rush, the team relies on quick fixes that replace proper solutions when the management pushes for faster delivery, creating tech debt.

And of course, sprint after sprint, all of the small compromises add up. You defer writing tests. You duplicate code. You skip documentation.

 

Why Technical Debt Builds Up and How to Spot It

Common Causes: Deadline Pressure, Poor Code Reviews, and Unintentional Shortcuts

Tight project schedules or urgent demands force developers to take shortcuts and implement less-than-ideal solutions to meet deadlines. More often than not, these tight schedules are the beginning of the technical debt snowball effect. 

Teams under severe time pressures cut corners writing code to meet a deadline, and this creates a cycle. Each sprint adds a little more debt, and each release compounds the problem.

The result is that poor or sloppy coding gets promoted to production without proper testing and reviewing under the stress of strict delivery deadlines. 

Key Warning Signs: Slowing Velocity, Frequent Bugs, and Fear of Changing Code

When the same type of issues keep appearing, or simple changes take longer than expected, it’s usually a sign that technical debt is impacting your team’s output.

Features that used to take days now take weeks. The more debt in a codebase, the harder it is to add or change anything without side effects.

There’s also the issue of frequent bugs, plus bug fixes that include new bugs, both of which are a clear sign of runaway debt. Rollout failures, recurring hotfixes, or increased rollback frequency are forms of operational interest payments on your tech debt. 

Lastly, the fear of changing code, which usually happens when new team members struggle to understand the code or need heavy handholding. 

As a result, onboarding takes weeks instead of days. Developers hesitate before making changes because they can’t predict the consequences.

 

Proven Strategies for Reducing Technical Debt

Refactor With Purpose and Focus On High-Impact Areas First

Focus on refactoring the areas of your code that have the greatest impact on performance, security, and scalability. Not all debt deserves immediate attention, according to the Pareto Principle.

20% of a codebase is usually responsible for around 80% of development pain. Target that 20% first.  We did this for a client who simply couldn’t wait for a complete refactor.  His application was in such a state of extreme technical debt that he couldn’t onboard new clients.  We applied the critical updates first – to both the application and the underlying cloud infrastructure to get the application to a stable place.  This year we will address the complete refactor, with the pressure of the business alleviated.

Note: If you’re unable to locate the troublesome 20% or struggling to put it back together, Tangonet’s Solutions can help. 

The process we used to clear nearly 2 years of technical debt for a client looked like this:

 

1. 1. Assessment & Prioritization: We first thoroughly audited their codebase and infrastructure. This gave us a clear understanding of where the technical debt lay and which issues were the most critical.
      

1. 1. Team Integration: To accelerate progress, we deployed a dedicated nearshore team from Tangonet Solutions to work alongside the client’s in-house team. This elastic approach allowed for a quick ramp-up without overwhelming their existing resources.
      

1. 1. Parallel Development: Instead of freezing all new development until the technical debt was cleared, we implemented parallel development. This meant that while a portion of the team worked on new features, another focused on removing technical debt.
      

1. 1. Code Refactoring: We began refactoring critical code areas to make them more maintainable and scalable for future needs. This resolved existing problems and created a more robust foundation for future updates.
      

1. 1. Regular Sprints and Reviews: Using Agile methodologies, we broke the backlog into manageable sprints and had daily stand-ups to keep the client informed and engaged. This ensured clear communication and quick feedback loops, avoiding the buildup of future debt.

Adopt CI/CD and Automated Testing to Prevent Future Debt

Automated tests ensure changes to the codebase don’t introduce new debt and provide a safety net for refactoring, as well as helping in bug detection. Pair that with CI/CD pipelines, and you’ll catch most issues before they even reach production. 

Refactoring requires a deliberate execution plan that uses incremental changes instead of attempting to complete every change in one large development. Small, continuous improvements prevent debt from snowballing into major problems.

Modernize Legacy Systems and Eliminate Outdated Dependencies

You should regularly assess the software stack and update outdated technologies to avoid accumulating technical debt due to unsupported or obsolete integrations. Legacy systems drag down development and create security risks. 

Note that modernization doesn’t mean rewriting everything at once. Tangonet helps companies modernize legacy systems without disrupting ongoing operations.

Our nearshore teams bring deep expertise in technical debt management, allowing you to modernize incrementally while maintaining business continuity.

Break Down Monoliths Into Modular, Maintainable Components

Modular architecture makes changes safer and faster. Components become easier to test, easier to understand, and easier to replace when needed. Well-structured modules with clear boundaries reduce the risk of changes in one area breaking functionality elsewhere.

 

Tools That Make Tech Debt Reduction Effective

You’re not required to use ALL of these tools. Some of them will be more useful than others, depending on your already existing framework and how it can integrate such tools. Yes AI-based tools matter – and they do speed up development when used judiciously.  However, over-reliance on these tools can be a trap, without expert oversight and review to ensure the inmates aren’t running the asylum 

Use Static Analysis (e.g., Sonarqube) to Quantify and Track Debt

SonarQube quantifies technical debt in terms of effort, estimating how long it would take to fix every issue. Each issue comes with an effort estimation showing the time required to fix it, which can help teams quantify their technical debt. 

The platform also aggregates remediation time into a total debt value, usually shown in hours or days.

Much like most static code analysis tools, SonarQube automatically scans a codebase to detect issues like coding standard violations, complex code constructs, and potential bugs that compromise code quality, maintainability, reliability, and security.

Teams using comprehensive static analysis tools can reduce their technical debt by 20 to 40% within just the first year, making it easier to prioritize improvements and demonstrate ROI to stakeholders. 

Quality gates automatically block merges or deployments when code violates standards, transforming analysis from passive observation into active gatekeeping.

Leverage Testing Frameworks (Jest, Cypress) and Dependency Managers (Dependabot)

Testing Frameworks

Cypress is designed for end-to-end testing of web applications and runs directly in the browser, and Jest is primarily a unit testing framework that works with JavaScript frameworks like React, Angular, and Vue. 

Both tools can be invaluable for you, as unit testing can quickly identify, isolate, and fix defects at the initial stages of development. This, once again, stops the technical debt snowball right from the beginning, plus a reduction of the debugging costs. 

Of course, you don’t use both at the same time, as that’s not cost-effective. Once again, you use what fits. 

Cypress runs in the same loop as the tested application with a Node server process providing constant communication and synchronization. This architecture allows Cypress to work at the network layer, reading and modifying web traffic as it flows.

On the other hand, Jest can execute tests in parallel, making it suitable for projects with a large number of unit tests.

Dependency Managers

Aging dependencies can add crippling amounts of technical debt to projects, and over time, once you get too far into that debt, it can be nearly impossible to climb out without major remediation. To solve this, consider trying Dependabot. 

Dependabot transforms dependency management from manual maintenance into an automated workflow, delivering proactive security updates and reducing technical debt.

Old dependencies are a form of technical debt because the further behind you fall, the harder it becomes to upgrade. 

Dependabot regularly checks dependencies for updates and creates pull requests with upgrades for approval, helping keep projects up to date and immediately applying security vulnerabilities when patches are released.

Track Debt Transparently With Jira, Confluence, and Observability Platforms

Jira and Confluence

Teams create a page in Confluence that outlines the team’s definition of technical debt, and these best practices guide future conversations around managing technical debt, reducing friction in software development in both planning and delivery.

This shared definition prevents confusion about what counts as debt versus what’s just new work that didn’t ship.

Each time there is unfinished work, create a Jira issue, give it a priority, estimate it, and work with the Product Owner to prioritize it on the backlog.

When technical debt sits in a separate tracker from feature work, teams lose the ability to make smart trade-offs between shipping new capabilities and maintaining code health.

Teams lose the strategic advantage of making priorities global across the product when technical debt is not in the same backlog as new features.

Jira’s dashboards help manage technical debt because they can visualize and keep track of it. This “two-dimensional” filter statistic gadget shows when debt was introduced and when the team expects to resolve it, giving stakeholders clear visibility into both the accumulation rate and the reduction of technical debt over time.

Monitoring Tools and General Observability 

Production monitoring reveals the real cost of technical decisions. Application performance monitoring tools track response times, error rates, and resource consumption patterns that surface problems before they escalate into customer-facing incidents. 

Error tracking platforms aggregate crash reports and exceptions, helping teams identify which parts of the codebase generate the most instability.

Deployment metrics matter just as much as application metrics. Teams that track deployment frequency, lead time, and rollback rates can spot patterns that indicate underlying code quality problems. When deployments start taking longer or failing more often, it signals debt that needs attention.

If your team struggles to maintain visibility across complex systems or needs help implementing comprehensive monitoring strategies, Tangonet’s DevOps specialists can integrate monitoring solutions that surface technical debt before it impacts your users. Our teams bring expertise in setting up comprehensive observability frameworks that work alongside your existing tools.

 

Sustainably Manage Technical Debt with the Right Nearshore Partner

Balance Feature Delivery and Maintenance with Smart Backlog Prioritization

Technical debt repayment should focus first on the liabilities with the greatest customer impact to align with business value. Reserve a fixed portion of each development cycle, 10 to 20% or more based on severity, for addressing technical debt. This approach prevents debt from accumulating faster than your team can address it.

A unified backlog ties everything together, aligning technical debt and feature work under one system. Separate backlogs lead to technical work getting deprioritized every sprint. When debt competes on equal footing with features, priorities become clear.

The challenge isn’t understanding what needs to happen. The challenge is having the capacity and expertise to execute consistently while meeting roadmap commitments.

Tangonet’s nearshore cloud engineering and Python teams provide the capacity to help you execute technical debt reduction strategies without sacrificing velocity. Our engineers integrate with your existing workflows through agile teams that own the refactoring process, staff augmentation to expand your capacity, or fractional teams to handle specific technical improvements—so you can reduce debt while keeping your roadmap moving.

Ready to Build Cleaner, Faster, More Scalable Software?

Minimizing technical debt starts with a conversation about where your team is today and where you need to be tomorrow.

Schedule a technical debt discovery call to discuss your challenges and explore how our nearshore teams can help you reduce debt, improve velocity, and build software that scales.