Managing technical debt

Technical debt is probably one of developers' biggest concerns (it's my personal pet peeve). It creeps in over time throughout a project: a shortcut taken to meet a deadline, tests postponed until later, an architecture that met yesterday's needs but is now showing its limits.

If left unchecked, it slows down development, makes maintenance more difficult, and hinders the evolution of applications. Fortunately, it can be contained and even reduced. Let's see how to tame the beast.

What is technical debt?

Technical debt refers to all the voluntary or constrained technical decisions made during the development of software, an application, or an IT project that favor a short-term objective over long-term optimization and will need to be corrected or improved later.

Like financial debt, it can be useful in the short term to move faster (developers are often under pressure to meet deadlines...), but it generates additional costs over time: code that is harder to maintain, more frequent bugs, slower evolution... A small change can sometimes take several days instead of just a few hours!

Good technical debt management is about finding the right balance between development speed and code quality to ensure the long-term sustainability of the project.

The different types of technical debt

Technical debt comes in many forms and is not limited to code. It affects the entire software lifecycle, from design to production.

Code and design debt is one of the most common types from my point of view as a developer. It appears when the code becomes difficult to maintain or when the original architecture limits future evolution. Code duplication (fortunately, I no longer have colleagues addicted to copy-paste), excessive complexity (although I still have one who likes making complicated things even more complicated...), poor component design, or inappropriate technical choices can all slow down development.

Test and documentation debt reflects a lack of quality around the project. Insufficient test coverage increases the risk of regressions, while outdated documentation makes the system harder to understand and knowledge sharing within the team more difficult.

Infrastructure and dependency debt appears when tools, environments, libraries, or frameworks are not kept up to date. It can lead to compatibility issues, security problems, or more complex deployments.

Finally, security and organizational debt relates to development practices and processes. Unfixed vulnerabilities, a lack of standards, poor communication between teams, or decisions made without a long-term vision can seriously impact the sustainability of a project.

Of course, there are other types of technical debt, but we've covered the main ones.

The real cost of technical debt

The velocity tax is particularly significant. In my experience, developing new features can sometimes take almost twice as long when software is heavily affected by technical debt. You may spend six months delivering a feature while your competitors ship it in three. This creates a vicious cycle: lower velocity encourages shortcuts, shortcuts create more technical debt, and that debt slows velocity even further.

Direct costs include longer development times, expensive bug fixes, and slower onboarding of new developers (it's not easy joining a project when everything is all over the place...). Indirect costs are even greater: delayed revenue, customer loss, employee turnover, security incidents, and compliance issues.

How to measure technical debt

Technical debt can be measured using technical indicators, but also through feedback from the teams dealing with it every day. Code quality metrics help identify risky areas, including cyclomatic complexity, code duplication, and the Technical Debt Ratio, which estimates the effort required to fix existing issues.

Test coverage is also an important indicator of software reliability. A high percentage is not a guarantee of quality, but insufficient coverage of critical features increases the risk of regressions.

Velocity metrics, such as a slower delivery pace, more time spent fixing bugs, or less frequent deployments, can reveal the impact of technical debt on the team.

Finally, developers' day-to-day experience remains an essential indicator, as daily frustrations and recurring roadblocks often reveal problems that metrics alone cannot detect.

How to prioritize technical debt

Technical debt should be prioritized according to its impact, risk, and the effort required to fix it. The Impact/Effort matrix helps identify quick wins, strategic initiatives, and lower-priority work. Security issues, performance problems, and anything that blocks product evolution should generally come first.

To prevent technical debt from piling up, teams can regularly dedicate part of their development capacity (for example, 10 to 20%) to reducing it alongside new feature development. Then again, depending on how bad the debt is, sometimes it simply needs a serious push.

How to reduce technical debt

Reducing technical debt depends on its nature. For legacy systems, the Strangler Fig Pattern allows existing functionality to be replaced gradually without a complete rewrite. Refactoring then improves code quality by simplifying its structure and continuously applying good practices.

Test-related debt can be reduced by gradually improving coverage of critical features, automating tests, and integrating checks into deployment pipelines. The goal is to improve software reliability without slowing down its evolution.

How to prevent technical debt

The best way to manage technical debt is to limit it from the very beginning. QED! Code reviews, automated quality checks, and a clear Definition of Done help maintain a consistent level of quality.

Code reviews encourage knowledge sharing and quickly identify problems. Unfortunately, they require available resources. Automated analysis tools can also block risky changes, such as security vulnerabilities or code quality degradation.

Finally, Architectural Decision Records (ADRs) help document technical decisions, their trade-offs, and any accepted technical debt, making it easier to track over time (especially on very large projects).

Conclusion

Technical debt is not an engineering failure. It is inevitable when developing software under real-world constraints, especially in smaller companies with limited resources.

The key is to find the right balance between controlling or reducing technical debt and continuing to deliver value.

Good luck to everyone dealing with technical debt that's gone off the rails 😉