There’s a moment that almost every development team knows. The moment when adding a feature that should take two days ends up taking two weeks. The moment when every change to the code breaks something else. The moment when nobody wants to touch a certain part of the system because nobody really knows how it works and everyone’s afraid of what might happen if something goes wrong.
That moment has a name: it’s when the technical debt is collected. And when it’s collected, it’s collected with interest.
Technical debt is one of the most important and least managed concepts in software development. Not because teams are unaware of it, but because it’s silent, cumulative, and always seems less urgent than the next feature, the next release, the next deadline. Until it ceases to be silent and becomes the main obstacle preventing the team from moving forward.
At Aufiero Informática, we work with development teams of varying sizes and across different sectors, and poorly managed technical debt is one of the most frequent causes of project delays, frustrated teams, and products that fail to scale. This article is a practical guide to understanding what technical debt is, how to identify it, how to measure it, and, above all, how to manage it before it paralyzes the team.
What is technical debt: the origin of the concept
The term was coined by Ward Cunningham, one of the pioneers of the agile movement, in 1992. The analogy is so accurate that it survived intact for more than three decades: technical debt works exactly like financial debt.
When a company takes on debt, it gains something now in exchange for paying more later. If it invests it wisely, the return justifies the cost. If it accumulates it without management, the interest payments begin to consume resources that should be generating value.
The same applies to code. When a team takes a shortcut, implements a quick fix instead of the right one, or stops refactoring because of deadline pressure, they’re incurring technical debt. They gain speed now at the cost of higher development time, more bugs, greater difficulty in making changes, and increased risk with each deployment.
The problem isn’t taking on technical debt. Sometimes it’s the right decision: getting to market faster can be more valuable than having perfect code. The problem is not tracking it, not measuring it, and not having a plan to pay it off.
Types of technical debt: not all debt is the same
One of the reasons technical debt is so difficult to manage is that it is not a homogeneous phenomenon. It takes different forms, has different origins, and has different levels of urgency.
Deliberate debt
This is the debt that is consciously taken on. The team knows the solution isn’t ideal, but decides to implement it this way for valid reasons: time to market, limited resources, uncertainty about requirements. This is the most manageable debt because it’s known from the beginning and you can plan how to repay it.
Unnoticed debt
It’s the kind of error that appears without the team’s intentions. It stems from a lack of knowledge, bad practices that went undetected, or code that seemed correct but wasn’t. It’s more difficult to manage because it first needs to be discovered.
Environmental debt
It’s not in the application code itself, but in the surrounding infrastructure: outdated language or framework versions, unmaintained dependencies, and server configurations that haven’t been updated in years. It’s especially dangerous because it creates security vulnerabilities in addition to maintenance problems.
Architectural debt
It’s the most expensive and the hardest to pay for. It happens when the original architectural decisions can no longer withstand the system’s growth. A monolith that should have been converted into microservices two years ago. A database that doesn’t scale with the current volume of data. An architecture designed for 100 simultaneous users that now handles 100,000.
Testing debt
Code without test coverage, tests that no longer maintain and that no longer prove what they should, and a lack of integration or performance tests. This debt multiplies the risk of any change and slows the team down because every deployment is a gamble.
How it accumulates: the most common mechanisms
Technical debt rarely appears suddenly. It accumulates gradually, through patterns that repeat themselves time and again in teams of all sizes.
Deadline pressure. It’s the most classic mechanism. There’s a fixed delivery date, time runs out, and the team takes shortcuts. What’s planned as a temporary solution becomes permanent because after one deadline, another always follows.
The lack of code review. Without systematic code reviews, bad practices take root undetected. Code that isn’t reviewed by others tends to accumulate problems that are only discovered much later.
Unplanned growth. A system designed for one purpose that gradually accumulates unforeseen functionalities ends up being a system not designed for its intended function. The original architecture creaks under the weight of years of additions.
Team turnover. When the developers who built a system leave, the knowledge about why certain decisions were made goes with them. The team that comes in later inherits code they don’t fully understand and makes decisions without the necessary context.
Lack of documentation. Undocumented code is code that only the person who wrote it can maintain. When that person isn’t around, the time it takes to understand what it does and why multiplies the cost of any changes.
Outdated dependencies. Updating libraries and frameworks is work and can break things. That’s why it gets postponed. And each version that’s skipped makes the next update more difficult and riskier.
How to measure technical debt
What isn’t measured can’t be managed. Technical debt has a nasty habit of being invisible until it’s too big to ignore, and one of the reasons is that few teams have concrete metrics to quantify it.
These are the most effective ways to measure it.
Static code analysis
Static analysis tools examine code without running it and detect problems such as high cyclomatic complexity, code duplication, standards violations, overly long functions, circular dependencies, and a long list of code smells that are indicators of technical debt.
SonarQube is the most widely used tool for this. It generates a detailed report of technical debt in estimated work hours to resolve it, classifies problems by severity, and shows how it evolves over time. Integrating it into the CI/CD pipeline allows for the detection of new debt before it reaches production.
Test coverage
The percentage of code covered by automated tests is one of the most direct indicators of technical debt. A codebase with 20% coverage has a completely different level of risk than one with 80%. Tools like Jest , pytest , or JUnit generate coverage reports that can be integrated into the pipeline and set as minimum thresholds for approving a merge.
Cycle time and lead time
If the time to implement similar features is progressively increasing, it’s a clear sign that technical debt is growing. Tracking these metrics over time reveals trends that the team may not be noticing on a daily basis.
Frequency and distribution of bugs
A system with high technical debt tends to generate more bugs, and these bugs tend to concentrate in the most problematic parts of the code. Analyzing the source of bugs that reach production allows you to identify the areas with the highest debt.
Maintainability Index
Some tools calculate a maintainability index for each module or code file, based on metrics such as complexity, code volume, and nesting depth. This index allows you to prioritize which parts of the system need the most urgent attention.
The Boy Scout camp rule
A more qualitative but very useful metric: Is the code better or worse after each intervention? If every time someone touches the code they leave it a little worse than they found it, the code debt is growing. If the Boy Scout rule is applied—always leaving the code a little better than it was found—the code debt will organically decrease.
How to manage technical debt: strategies that work
Measuring technical debt is the first step. Managing it is the real challenge. These are the strategies that work best in practice.
Make it visible
Technical debt that isn’t recorded doesn’t exist for the organization. The first step is to take it out of informal team conversations and bring it to where decisions are made: the backlog, management reports, and conversations with product and business teams.
Creating specific technical debt tickets in the team management system, whether it’s Jira, Linear, Asana, or any other tool, with estimates of impact and resolution cost, is essential so that it can be prioritized along with the rest of the work.
Dedicate systematic capacity
Technical debt will never be paid off if it’s only managed when there’s time. And there’s never time. The only way to reduce it sustainably is to explicitly reserve capacity for it in every sprint or work cycle.
The most common models are to allocate a fixed percentage of the team’s capacity, between 15% and 20%, to technical debt reduction work, or to alternate feature sprints with consolidation sprints where the focus is on improving the quality of existing code.
Prioritize by impact, not by age
Not all technical debt is equally urgent. Debt that is on the critical development path, that generates frequent bugs, that hinders scalability, or that poses a security risk must be addressed first. Debt located in parts of the system that no one touches can wait.
An impact vs. effort matrix helps prioritize: high-impact, low-effort debt comes first; high-impact, high-effort debt requires planning; low-impact debt can be left in the backlog for when capacity is available.
Integrate quality into the process
The best strategy for managing technical debt is to avoid accumulating it. This requires integrating quality practices into the team’s daily workflow.
Systematic code reviews as a requirement for all merges. Definition of coding standards and linting tools that automatically apply them. Minimum test coverage thresholds that prevent merges from lowering them. Static analysis in the CI/CD pipeline to detect new problems before they reach the main branch.
These practices do not eliminate existing technical debt, but they prevent it from growing further while the team works to reduce it.
Continuous refactoring vs. large migrations
There are two approaches to paying off technical debt: continuous refactoring and large migrations. Both have their place, but continuous refactoring is generally more sustainable.
Continuous refactoring involves incrementally improving code, using each intervention to make that module or function a little better than it was before. It doesn’t require separate time or special planning: it’s a professional approach applied to every task.
Large-scale migrations—rewriting an entire module, migrating from one framework to another, redesigning an architecture—are necessary when the debt is so deep that incremental refactoring is insufficient. However, these are high-risk projects that require careful planning, clear communication with the business, and a transition strategy that allows for continued value delivery during the migration.
Communicate in business terms
One of the biggest challenges in managing technical debt is convincing non-technical people that it’s worth investing time and resources in something that doesn’t produce visible features.
The key is to translate technical debt into terms the business understands. This means not talking about cyclomatic complexity or test coverage, but rather how much extra time each feature takes due to accumulated debt, how many bugs reach production and what their cost is, and how quickly the system can scale if the business grows by 50% next year.
When technical debt becomes a conversation about delivery speed, operational risk, and growth capacity, it ceases to be an exclusive problem for the engineering team and becomes a business decision that the organization can and should make.
The tools that make work more manageable
Technical debt management relies on an ecosystem of tools that cover different aspects of the problem.
SonarQube for continuous static analysis and technical debt measurement in concrete terms. It integrates with virtually any CI/CD pipeline and supports most relevant programming languages.
GitHub Actions or GitLab CI to automate quality analysis on each pull request, ensuring that debt does not grow with each merge.
Jira or Linear to register, estimate and prioritize technical debt along with the rest of the backlog, giving it organizational visibility.
Codecov or Istanbul to track test coverage over time and set thresholds that the pipeline can automatically enforce.
Dependabot or Renovate to manage dependency updates automatically, preventing the library ecosystem from becoming outdated without anyone noticing.
Datadog or New Relic to monitor the behavior of the system in production and identify areas of performance degradation that are usually indicators of technical debt in the corresponding components.
When is technical debt an emergency?
There are signs that technical debt has ceased to be a management problem and has become an emergency requiring immediate action.
When the team needs more time to estimate any change because no one fully understands the potential impact. When production bugs are becoming frequent and unpredictable. When onboarding a new developer to the project takes months instead of weeks. When the team starts losing its best developers, who prefer working on projects with cleaner code. When the organization can’t launch to market quickly enough to compete.
In these cases, the cost of inaction is already higher than the cost of action. And intervention requires not only technical decisions but also an explicit commitment from the organization to dedicate the necessary resources for as long as needed.
In conclusion: technical debt is a business decision
Technical debt is not a technical problem. It’s a business decision with technical consequences. And like any business decision, it can be made well or poorly, with or without information, with a management plan or by letting it grow until it becomes unmanageable.
Teams and organizations that learn to manage technical debt systematically aren’t the ones that never accumulate it. They’re the ones that know how much they have, understand the cost of having it, and make conscious decisions about when to pay it off and when to let it run a little longer.
At Aufiero Informática, we support development teams in implementing these practices and tools. If your team is feeling the weight of accumulated technical debt and you want to understand where to start, we’re here to help.
Does your team already have a process for managing technical debt? What strategies worked for you and which didn’t? Let us know in the comments.
