Technical debt usually gets attention in two situations: when releases become painful, or when even a small change starts costing far more than it should. Until then, it is often treated as background noise: some dependencies are outdated, some areas have no tests, there are known bugs, documentation is weak, but overall the system still works.
That is exactly why it becomes dangerous. Technical debt rarely arrives as one major event. It accumulates through a series of small decisions that look reasonable at the time. A library upgrade gets postponed because a feature matters more right now. Performance work is delayed because the SLA is still acceptable. Tests are skipped to hit a release date. Documentation is left for later. After a while, the team is no longer gaining speed from those compromises. It is paying for them in every sprint.
The main mistake is treating technical debt as something abstract. If it is not captured in tasks, estimated, and brought into planning, it effectively does not exist. And if it does not exist in planning, it keeps growing.
What technical debt means in real development
Without the polished definitions, technical debt is everything that makes changes to a system more expensive and more risky than they need to be.
This is not only about code. Debt can sit in architecture, test coverage, dependencies, documentation, user workflows, and even in the operational processes around the system. The real sign of debt is not that something looks messy. It is that the team keeps paying for it through time, defects, manual effort, or elevated risk.
A few phrases usually show that debt has already become part of daily work:
- “better not touch that module”
- “release carefully, there are no tests there”
- “yes, the library is old, but upgrading it is risky”
- “that bug is known, just use the workaround”
- “ask Ivan if you need it, he is the only one who knows how it works”
Once this becomes normal, the debt is no longer affecting only the codebase. It is affecting the team’s delivery speed.
1. Outdated library versions
One of the most common sources of technical debt is old dependencies and framework versions that stay untouched for years because the system is still running.
The problem is that this kind of stability is usually misleading. As long as the library is not blocking work today, the upgrade gets pushed back. Then one day the project is sitting on an unsupported version, security fixes are no longer available, newer parts of the ecosystem are incompatible, and moving to a supported baseline turns into a separate migration effort.
The longer the upgrade is delayed, the more expensive it becomes. There is a major difference between moving one minor version forward and jumping across several major versions. The first is a normal engineering task. The second often becomes a risky migration with regression concerns, API compatibility issues, configuration changes, and hard-to-predict side effects.
There is also a quieter risk: hidden constraints begin to pile up. The team can no longer adopt a new observability agent, a newer JDBC driver, a Kafka client update, or a security patch without triggering a chain of dependent upgrades. On paper the issue is just an old library. In practice it becomes a constraint on the system’s evolution.
2. Poor performance
Technical debt is not only about code maintainability. Performance degradation becomes debt very quickly, especially when the team gets used to it and starts treating it as normal.
In most systems, it does not begin with a disaster. It starts with small compromises. One heavy query. One extra synchronous service call. One place where caching was skipped. One batch job that runs longer than expected. While the load is moderate, the system survives. Then the data volume grows, more users arrive, more integrations are added, and it becomes clear that the safety margin disappeared a long time ago.
At that point, performance stops being a narrow technical topic. It starts affecting the business directly: users wait longer, batch jobs no longer fit into their windows, infrastructure costs go up, and the team hesitates to add new scenarios because the system is already close to its limit.
The worst case is when there are no baseline metrics and no history of degradation. If the team does not know its p95 or p99, does not understand the load profile, and cannot point to actual bottlenecks, every conversation about performance turns into guesswork. That is already technical debt: the problem exists, but it is not described in a way the team can manage.
3. Lack of tests
A lack of tests is one of the most expensive forms of debt because it affects every change, not just one specific part of the system.
For a while, a system without tests can look faster to build. But that gain does not last. Over time, every change demands more manual checking, more coordination, and more caution. Refactoring becomes risky. Releases become stressful. A bug fix in one area can create a new defect in another.
The issue is not limited to the absence of unit tests. A weak test setup also creates debt. For example:
| Situation | What it turns into |
|---|---|
| Very few tests | regressions are found after release |
| Only integration tests | checks are too slow, so the team runs them inconsistently |
| Fragile tests | people stop trusting them |
| Tests tied to implementation rather than behavior | any refactoring breaks a large part of the suite |
| No coverage for critical business flows | the most important scenarios still rely on manual verification |
In the end, a team without tests does not just have weaker quality control. It loses the ability to change the system safely. That directly affects delivery.
4. Bugs and known defects
Not every bug is technical debt. But a bug that everyone knows about, nobody fixes, and people work around on a daily basis almost always becomes part of it.
That kind of defect rarely lives only in the code. It usually grows its own support structure around it: manual checks, special instructions for support teams, extra business steps, repeated operations, and exceptions in process rules. From the outside, it may look manageable. In reality, the team keeps paying for it.
The most dangerous cases are the defects people get used to. They stop feeling urgent because they do not look like an outage. But those are often the issues that drain time for years: support handles the same cases again and again, analysts build new processes around the limitation, and developers remember that one fragile area must be treated carefully.
If a bug already has an oral workaround, that is usually enough reason to move it into the technical debt backlog instead of leaving it in a loose list of known issues.
5. Documentation
Poor documentation is easy to ignore in a small team where everyone already shares context. But once the system grows, new people join, integrations multiply, audits happen, incidents occur, or several teams need to work in parallel, missing documentation starts slowing work down as much as bad code does.
The issue is not simply that there is nothing to read. The issue is dependence on individual memory. Architectural constraints, past decisions, integration quirks, hidden contracts, and operational details live only in the heads of a few engineers. As long as those people are around, the system looks manageable. The moment someone is on vacation, leaves the project, or is unavailable during an incident, the cost of that approach becomes obvious.
Documentation debt does not mean everything must be written down. In practice, it is usually enough to keep a few critical layers up to date:
- the architecture view and service boundaries
- key integrations and contracts
- a decision log for important technical choices
- instructions for running, supporting, and troubleshooting the system
- constraints that engineers must remember before making changes
This is not paperwork for its own sake. It is a way to transfer context and reduce bus factor.
6. User guide
This is often not treated as technical debt at all, especially in internal enterprise systems. In practice, though, the absence of a user guide creates measurable losses.
When users do not understand how to work with the system, the load shifts to support, analysts, and developers. Explanations move into chat threads, short calls, screen-sharing sessions, forwarded outdated instructions, and manual guidance for new scenarios. If the product is complex, such as a CRM, MDM platform, internal operational tool, or enterprise workflow system, those losses become visible very quickly.
There is also a second effect: changes become more expensive. Any change in the interface or process requires additional communication because users do not have a stable reference point in the form of current guidance.
A user guide is not cosmetic. In a mature product, it is part of the operating model. If it does not exist, the system starts depending on a live translator between functionality and the end user. That is a valid item for the technical debt backlog.
Why technical debt needs to be quantified
As long as technical debt lives in phrases like “everything is outdated here,” “this is a bottleneck,” or “that part is dangerous,” it will usually lose to product work during prioritization.
Not because product work is always more important. It loses because product work is usually described better: it has a goal, an expected effect, a deadline, and an outcome. Technical debt often has only team frustration attached to it.
To make debt manageable, it has to be converted into concrete units. A minimal template usually looks like this:
| Field | What to capture |
|---|---|
| Area | dependencies, performance, tests, bugs, documentation, user guide |
| Problem | what exactly is wrong |
| Consequence | what it affects: releases, SLA, support, team speed, risk |
| Risk | low / medium / high |
| Estimate | hours, days, story points |
| Success metric | how the team will know the situation improved |
For example, “we should update the stack” is not useful. “Spring Boot 2.x is no longer supported, some security patches are unavailable, and newer dependency versions are blocked; estimate: 8 working days; result: move to a supported baseline and remove upgrade constraints” is already good enough for planning.
Quantification is not about reporting for its own sake. It is what allows technical debt to be compared with other debt items and with product work in the same language.
A separate technical debt backlog
If technical debt is not kept in a separate list, it almost always dissolves inside the general task queue. In that form it is hard to discuss, hard to rank, and easy to ignore.
That is why a separate technical debt backlog works better in practice. It should not be isolated from normal planning. It should be part of it.
Each item in that backlog should have at least three things: a clear description of the problem, an estimate, and a reason for its priority. For prioritization, a few simple criteria are usually enough:
| Criterion | Question |
|---|---|
| Business impact | does it affect customers, operations, or SLA |
| Delivery impact | does it slow down or destabilize delivery |
| Risk | can it lead to incidents, defects, vulnerabilities, or data loss |
| Cost of delay | how much more expensive will it be to fix next quarter |
| Effort | what is the real cost of doing the work |
This also helps remove emotion from the discussion. Instead of saying “everyone hates this part of the system,” the conversation moves to risk, cost, and impact.
Example of a technical debt backlog
A working backlog might look like this:
| Task | Reason | Priority | Estimate | Expected effect |
|---|---|---|---|---|
| Upgrade critical libraries and the framework | security and compatibility risks have accumulated | High | 8 days | fewer vulnerabilities, easier maintenance |
| Optimize the 5 slowest SQL queries | p95 is above target SLA | High | 5 days | faster core operations, lower database load |
| Add integration tests for a critical business flow | regressions keep appearing in releases | High | 6 days | more predictable releases |
| Fix known defects with manual workarounds | support and business teams are compensating in process | Medium | 4 days | fewer manual operations and support requests |
| Document key architectural decisions and integrations | too much knowledge depends on a few engineers | Medium | 3 days | faster onboarding and easier incident analysis |
| Prepare a user guide for the top 5 scenarios | support load is too high | Medium | 3 days | fewer repeated questions and user mistakes |
At this point, it is no longer just a list of complaints. It becomes a list of tasks with meaning, cost, and expected results.
Why it makes sense to spend 20% of each sprint on debt
Trying to “fully eliminate technical debt first and return to product work later” almost never works. The business is usually not ready to stop product development for months, and without clear boundaries the team can easily drift into endless internal improvements with no visible result.
That is why reserving part of each sprint for technical debt is a practical approach. Starting with 20% is usually reasonable: not so little that nothing changes, and not so much that product delivery stalls.
There are several benefits to this.
First, it makes the work predictable. The team does not need to re-argue every sprint why technical debt deserves time.
Second, it reduces accumulation. Small, regular improvements are almost always cheaper than occasional large-scale cleanup efforts.
Third, after a few months the impact becomes visible in numbers: fewer incidents, fewer regressions, faster releases, less manual work, shorter onboarding, and more stable performance.
Of course, 20% is not a universal number. One team may need only 10 to 15%. Another team, especially in the middle of a heavy migration, may need 25 to 30%. But the principle of reserving capacity works better than relying on the idea that there will be a convenient moment later.
How to put this into practice
The mechanics are simple.
Start by collecting known debt items into one list. Do not try to fix everything immediately. Just make it visible. Then record the impact, risk, and estimate for each item. After that, rank the backlog and bring into the sprint work that fits roughly 20% of the team’s capacity.
The next step is to keep the work measurable. If the team takes on performance debt, it should know which metrics are expected to move. If the work is about tests, it should be clear where regression risk should go down. If the effort is about documentation, it should be clear which dependency on specific people is being reduced.
One more point matters: the backlog needs regular review. Some items lose importance over time. Others become more urgent. In some cases, an architectural change removes an old debt item entirely. In others, new debt appears that was not visible before.
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