By Priya Shankar, Head of Product at Glue
Every engineering leader knows technical debt exists in their codebase. Few can point to exactly where it lives, how much it costs, or which pieces to fix first. This gap between knowing and seeing is the core problem with technical debt visibility, and it is the reason most organizations either ignore debt entirely or throw resources at the wrong parts of it.
I have been in planning meetings where the team agreed "we need to address tech debt" and then could not agree on what that meant. Without visibility, technical debt stays an abstract complaint rather than a concrete problem to solve.
Why Tech Debt Is Invisible
Technical debt is invisible by default because it does not manifest as a single, measurable thing. It shows up as slowness. Features take longer to build than they should. Bugs recur in the same modules. Deployments require manual workarounds. But none of these symptoms point to a specific root cause without deeper analysis.
There are structural reasons why tech debt hides so effectively:
It accumulates gradually. No one commit creates a debt crisis. Instead, hundreds of small compromises, shortcuts taken under deadline pressure, dependencies not updated, tests not written, compound over months and years. The codebase degrades so slowly that the team adjusts to the friction without recognizing its source.
It lives in the code, not in tickets. Most project management tools track planned work. Technical debt is, by definition, unplanned work that results from past decisions. It does not have tickets. It does not appear in sprint boards. It exists only in the codebase itself, visible only to engineers who happen to work in affected areas.
It is politically inconvenient. Acknowledging tech debt means acknowledging that past decisions created problems. In organizations where velocity is king, admitting that the codebase needs remediation feels like admitting failure. So teams quietly work around debt rather than making it visible.
According to a FullScale survey, 35% of CTOs cite technical debt as their number one engineering challenge. Yet most cannot quantify it. This disconnect between awareness and measurement is exactly what makes debt so dangerous.
Metrics That Make Debt Visible
If you cannot see technical debt, you cannot prioritize it. The good news is that your codebase already contains signals that reveal where debt lives. You just need the right metrics to surface them.
Code Churn Rate
Churn measures how frequently files change. A file that gets modified in every sprint is either under active development or is a source of recurring problems. High churn in modules that are not part of active feature work is a strong signal of accumulated debt: code that keeps needing fixes because it was not built well the first time.
Coupling Density
Coupling measures how interconnected your modules are. When changing file A requires changes to files B, C, and D, those files are tightly coupled. High coupling makes every change expensive because the blast radius extends far beyond the intended scope. Kong's research indicates that technical debt can reduce development speed by 30%, and coupling is one of the primary mechanisms through which that slowdown occurs.
Ownership Concentration
Modules with a single contributor represent both a knowledge risk and a debt risk. Code written by one person over a long period often reflects that person's evolving understanding, complete with inconsistencies, abandoned approaches, and implicit assumptions that are not documented. When someone else needs to work in that area, the hidden debt surfaces as confusion and bugs.
Test Coverage Gaps
Low test coverage is not debt by itself, but it is a debt amplifier. Areas with poor coverage accumulate debt faster because changes are made without a safety net. They also make existing debt harder to fix because refactoring without tests is risky.
Build and Deploy Friction
If your CI pipeline takes 45 minutes, that is debt. If deploying requires manual steps, that is debt. If certain services can only be tested in staging because local development setups are broken, that is debt. These operational frictions compound daily across every engineer on the team.
Research from Jellyfish shows that engineering teams spend 23-42% of their development time on maintenance and debt-related work. Measuring these metrics turns that abstract percentage into a concrete map of where time is being lost.
Building a Tech Debt Dashboard
Individual metrics are useful. A dashboard that combines them is transformative. Here is how to build one that actually drives action.
Layer 1: Codebase Health Signals
Start with automated analysis of your repository. Map churn rates, coupling patterns, ownership concentration, and complexity scores across your entire codebase. This creates a baseline view of where debt is concentrated.
Glue generates this layer automatically by analyzing your Git history and codebase structure. It produces file-level and module-level health scores that update as your codebase evolves, giving you a living map of technical debt rather than a point-in-time snapshot.
Layer 2: Impact Correlation
Raw code metrics become powerful when you connect them to business impact. Correlate high-churn, high-complexity areas with incident frequency, bug reports, and feature delivery timelines. This answers the question "what is this debt actually costing us?"
For example, if your payment module has a churn rate 3x the codebase average and is also the source of 40% of your production incidents, that is a clear signal that debt in that module is directly affecting reliability. Kong estimates that 40% of IT budgets go toward maintenance rather than innovation. Your dashboard should show exactly which modules are consuming that maintenance budget.
Layer 3: Trend Lines
A single snapshot tells you where you are. Trend lines tell you where you are heading. Track your debt metrics over time to see whether things are improving or getting worse. If coupling density in your core API is increasing sprint over sprint, that is an early warning signal that your team should address before it becomes a crisis.
Layer 4: Team Overlay
Overlay team and ownership data on your debt map. This shows you not just where debt exists, but who is affected by it and who has the context to fix it. A high-debt module owned by a senior engineer with bandwidth is a different situation than a high-debt module owned by a single junior developer who is already overloaded.
Glue's engineering leader dashboard combines all four layers into a single view. You can see code health, ownership risks, complexity trends, and team dependencies in one place, without requiring engineers to manually report on debt.
Prioritizing What to Fix
Visibility without prioritization just produces anxiety. Once you can see your technical debt, you need a framework for deciding what to tackle first.
The Impact-Effort Matrix
Not all debt is worth fixing. Some debt lives in code that rarely changes and does not affect development speed. Other debt sits in hot paths that the team touches every sprint. Prioritize based on the intersection of two factors:
- Impact: How much does this debt slow the team down? Measured by churn rate, bug frequency, and developer time spent in the area.
- Effort: How much work is required to remediate? Measured by coupling density, test coverage, and scope of changes needed.
High-impact, low-effort debt goes first. That is where you get the most improvement per engineering hour invested.
The Strategic Alignment Filter
After impact-effort ranking, apply a strategic filter. Debt in modules that are on your roadmap for the next quarter should be prioritized over debt in stable, rarely-touched areas. Fixing debt right before you build on top of it gives you immediate returns.
Glue helps with this alignment by connecting your technical debt tracking to your planned work. When you can see that your next quarter's biggest feature touches a module with high debt scores, you can schedule remediation as part of the feature work rather than as a separate, competing initiative.
The Quick Wins Approach
Dedicate 10-15% of each sprint to small debt fixes. Not large refactoring projects, but targeted improvements: adding tests to uncovered hot paths, reducing coupling in one specific area, updating a dependency that is causing build warnings. These accumulate over time and prevent the "we will address debt next quarter" trap that never materializes.
Track the results. If your code health scores in targeted areas improve over three sprints, you have evidence that the investment is working. If they do not, adjust your approach. Without visibility into technical debt, you cannot even tell whether your remediation efforts are having an effect.
Building Organizational Buy-In
The biggest barrier to fixing technical debt is not engineering time. It is organizational prioritization. Product teams push for features. Leadership wants velocity. Debt work feels invisible because its outputs are not user-facing.
Technical debt visibility solves this by making debt costs concrete and shareable. When you can show that a specific module causes 3x more bugs than the codebase average, that your team spends 30% of each sprint on maintenance in a specific area, and that fixing it would reduce cycle time for upcoming features, the conversation changes from "can we afford to fix debt?" to "can we afford not to?"
See your technical debt before it sees you. Try Glue free and get a complete debt visibility dashboard for your codebase.
Frequently Asked Questions
How do you make technical debt visible?
You make technical debt visible by analyzing your codebase for concrete signals: code churn rates, coupling density, ownership concentration, test coverage gaps, and build friction. Tools like Glue automate this analysis by mapping your repository's structure and Git history, producing file-level and module-level health scores. The key is moving from "we have debt somewhere" to a specific, data-backed map of exactly where debt lives and how much it costs.
What metrics measure technical debt?
The most useful metrics for measuring technical debt include code churn rate (how frequently files change outside active feature work), coupling density (how many files must change together), ownership concentration (bus factor per module), test coverage gaps, and CI/CD pipeline duration. These metrics, when combined and tracked over time, produce a reliable picture of where debt is accumulating and whether remediation efforts are working.
How do you prioritize technical debt?
Prioritize technical debt using an impact-effort matrix. Rank debt items by their impact on team velocity (measured by churn, bug frequency, and developer time) and the effort required to fix them (measured by coupling, scope, and test coverage). Focus first on high-impact, low-effort items. Then apply a strategic filter by prioritizing debt in modules your team will build on in the coming quarter. Dedicate 10-15% of each sprint to small, targeted debt fixes rather than waiting for large remediation projects.