In the realm of software engineering, the term “technical debt” has long been a go-to explanation for many of the challenges we face in maintaining and evolving systems. It evokes frustration, slowdowns, and the sense that something was done wrong in the past that now demands our attention. But while nearly every developer, designer, and product manager has encountered technical debt, the definition of what it actually means varies widely. In fact, it may be time to reconsider how we talk about—and manage—technical debt, moving towards a more precise and actionable framework.
Understanding the Ambiguity of Technical Debt
The phrase “technical debt” is often used as shorthand for any issue related to inefficiencies in a system, but this broadness is its downfall. Ask ten people what technical debt means, and you’re likely to get ten different answers. For developers, it may mean suboptimal or “bad” code. For product managers, it’s the root cause of lost weeks in development schedules. Designers often see it as the reason their ideal UI cannot be implemented.
This variability in definition creates communication breakdowns. When engineers ask for time to address technical debt, the business may recall past instances where no tangible improvement was seen after such efforts. This disconnect breeds mistrust, particularly when weeks are dedicated to “tech debt sprints” that fail to noticeably improve development velocity.
Why Equating Technical Debt to “Bad Code” is a Trap
One of the biggest misconceptions is equating technical debt solely with bad or poorly written code. While code quality is an important factor, it is dangerous to assume that previous developers simply failed in their jobs. Often, the decisions that lead to technical debt were made under constraints—whether time, resources, or even legal requirements—that were valid at the time.
For instance, I worked on a project where our team was frustrated with a database design that required pulling data from two tables for a single query. We assumed this was an oversight, or that inertia had kept the poor design in place. After a significant amount of discussion about how to “fix” it, we learned from a product manager that the design was due to privacy regulations; combining the data into a single table was, in fact, illegal.
This story highlights two major points:
- Technical debt often arises from external constraints, not just bad coding practices.
- Misidentifying the source of technical debt leads to wasted effort. We had planned an overhaul that could have created compliance issues.
Moreover, viewing technical debt as synonymous with bad code can lead to another dangerous assumption: that by writing perfect code, we can avoid technical debt altogether. This mindset neglects the importance of revisiting, documenting, and maintaining even well-written code. Over time, even the best code can become outdated or unmanageable if not stewarded properly.
Shifting the Conversation: From Technical Debt to Maintenance Load
Rather than broadly labeling all inefficiencies as technical debt, a more effective approach is to measure and manage the maintenance load of a system. Maintenance load refers to the time and effort spent on tasks that don’t directly contribute to new feature development but are necessary to keep the system running smoothly. This concept gives us a concrete, measurable metric to discuss with stakeholders outside of engineering.
Here’s how focusing on maintenance load benefits the team:
- Business alignment: If 50% of your development team’s time is spent on maintenance, then your feature planning should assume half capacity. This reframing helps business leaders understand the impact of maintenance on feature delivery and motivates them to invest in reducing this load.
- Measurability: Maintenance load can be tracked over time. A system with increasing maintenance load will create more friction for developers, reducing their capacity to work on new features. By monitoring this, teams can better forecast when intervention is needed.
Reducing Maintenance Load: Practical Strategies
To reduce maintenance load and ensure long-term productivity, teams should adopt code stewardship practices that prioritize maintainability over time. This includes:
- Documentation and Context Preservation: Developers often face difficulties when trying to work with code written by others, especially after key team members leave. This can result in “context loss events,” where critical knowledge about the system’s design or behavior is lost. Documenting key decisions, maintaining clear and up-to-date documentation, and ensuring onboarding processes capture system knowledge are vital steps toward reducing future maintenance burdens.
- Framework and Library Updates: Regular updates to libraries and frameworks can prevent massive headaches later on. By allowing updates to accumulate, the complexity of debugging increases, and teams become less familiar with the latest changes. Instead, schedule regular updates and document their effects on the system to minimize disruption.
- Addressing Architectural Issues: Often, technical debt stems from architectural decisions made in the past based on outdated assumptions about the domain. When your system continually struggles with the same architectural bottleneck, it may be time to rearchitect that portion of the system. This process should be informed by the types of changes expected in the future, ensuring flexibility in key areas.
The Importance of “Debt Weeks” and Housekeeping Ceremonies
One common approach teams use to manage technical debt is dedicating specific time to tackle it, often referred to as a “debt week” or “housekeeping ceremony.” These are structured intervals where teams pause feature development to focus entirely on reducing technical debt. When executed effectively, debt weeks can offer the following benefits:
- Focused Remediation: By dedicating a specific block of time to tackle issues, developers can address pressing concerns without the distractions of feature deadlines. This focused effort often leads to more impactful improvements compared to tackling debt sporadically.
- Alignment and Communication: Debt weeks force teams to articulate the most critical issues facing the codebase, and communicate them effectively to stakeholders. This creates better understanding across teams, including product managers and business leaders, about why technical debt reduction is necessary.
- Long-Term Velocity Boost: By reducing the maintenance load during these dedicated periods, teams are able to work more efficiently in the long run. Features can be developed faster, and unexpected slowdowns due to hidden technical debt are minimized.
However, there are potential pitfalls. Debt weeks can become ineffective if:
- Developers treat them as an opportunity for personal refactoring projects rather than focusing on the most pressing issues.
- The effort spent during debt week doesn’t result in measurable improvements, leading business stakeholders to lose confidence in its value.
To avoid these issues, it’s critical to identify and prioritize tasks that directly impact the maintenance load and feature development process. Using measurable metrics, such as time spent on maintenance, can help demonstrate the effectiveness of these focused efforts.
Key Metrics: Tracking Maintenance Load Over Time
The goal is to not only reduce maintenance load but also slow its growth. Ideally, teams can aim for negative growth, where maintenance becomes easier over time rather than harder. This requires actively tracking maintenance tasks, identifying their sources, and addressing the root causes of complexity.
For example, are developers constantly diving into old repositories to make changes? This indicates a lack of documentation or outdated systems. By addressing these issues early, the team can avoid larger setbacks in the future.
Moving Beyond the “Tech Debt” Mindset
Instead of lumping all inefficiencies under the banner of “technical debt,” shifting to a mindset focused on specific, measurable maintenance tasks allows teams to engage in more productive conversations. These tasks, framed as investments in future feature velocity, give engineers a clearer framework for discussing the real reasons behind slowdowns. Moreover, this approach encourages recognition of the value in maintenance work—something often overlooked in favor of new feature development.
By focusing on reducing maintenance load, teams can proactively tackle the factors that inhibit smooth, efficient development. This makes future work easier, improves overall system health, and aligns the interests of both business and engineering teams. In this way, we can move beyond the disheartening, amorphous discussions around technical debt and work toward measurable, sustainable improvements.