Also known as technical debt, it’s a concept in software development that represents the hidden cost of choosing a quick fix or short-term solution to meet immediate needs, rather than investing in a higher quality approach. Why does this happen? To speed up the release of a product.
In this guide, we’ll walk you through the essentials of technical debt and share some practical tips to help you manage it better. Let’s dive in.
How to eliminate it completely?
You can’t. That’s right, it’s impossible to eliminate technical debt completely. If you think wiping out all the technical debt in your project will solve everything, that’s a bit naive. Technical debt is inevitable. It’s always going to be there. The best you can do is manage it as intentionally as possible.
Take aging code, for example. It might seem harmless, but over time, it becomes technical debt. So if you or your team is fighting hard to have zero technical debt, that’s actually a red flag — and there’s even a name for it: Gold Plating.
Gold Plating
In project management, gold plating means delivering more than what was originally requested. It’s a common move, and one that many people still don’t realize can be harmful.
When you go beyond what the client asked for, it might feel like you’re going above and beyond to make them happy. But it can have the opposite effect. You risk delivering things they didn’t ask for or need, which can lead to frustration and confusion.
Trying to eliminate every last bit of technical debt often leads to gold plating in tech teams. And that can have real consequences, from bloated product costs and misaligned expectations to increased risk for both the project and the team.
Reasons why technical debt happens
Technical debt can come from a lot of places — not just time pressure, like we mentioned earlier, but also from more complex situations.
When a team is racing against the clock and dealing with super tight deadlines, quality often takes a back seat. The only focus becomes speed. That’s why setting unrealistic delivery timelines is one of the main causes of technical debt.
But there are other common reasons as well:
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Lack of technical knowledge
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Choosing the wrong technology
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Letting code age without updates
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Not using an iterative development process with feedback and testing from users
Types of technical debt
Besides the many causes, there are four types of technical debt. We can break them down using Martin Fowler’s “Technical Debt Quadrant” like this:
Reckless and deliberate: “We know there’s a problem, but we’re not going to fix it”
Reckless and inadvertent: “Working with a new programming language”
Prudent and deliberate: “We have a tight deadline, so we’ll ship it with this issue and fix it later”
Prudent and inadvertent: “Now that the project is done, we realize how we should have done it”
Technical debt in startups
When we talk about startups, we’re almost always talking about speed and testing hypotheses. That’s why a lot of companies end up accepting technical debt — sometimes a little too much — just to get their product out to market as fast as possible.
It’s a tricky topic, and to make sense of it, we need to split startups into two stages:
Before product-market fit
At this stage, the main goal is to validate the business model. And yes, speed really matters here. Shipping an MVP fast is key. In this phase, taking on some technical debt can be worth it, as long as it falls under the “prudent and deliberate” quadrant.
That’s because it’s important to be intentional about what gets left behind so it can be handled later without major issues. This is when having experienced developers and specialists makes a big difference.
Scaling up
At this stage, it’s important to avoid technical debt as much as possible — and even start paying down some of what was accumulated earlier. From now on, issues like performance bottlenecks, usability problems, and architectural limitations will start showing up, all as a result of past debt.
This is where you need to be extra careful. Like we said earlier, the more debt you carry, the harder it gets to maintain the project. From this point on, you need to raise the bar for code quality. Set clear standards, write even more tests, and invest in whatever’s needed to keep quality high.
The downside of AI in software development
AI is helping teams ship code way faster, but it’s also creating technical debt faster than a lot of teams can handle.
Research from GitClear shows that code duplication has increased nearly 8x in projects with heavy AI usage. Another study from Microsoft found that 84% of developers reported changes in their workflow with AI, but many still don’t fully trust the code it generates.
A study led by Stanford researchers backs this up. They looked at how developers interacted with AI coding assistants to solve security problems and found that:
- Users with access to AI wrote less secure code in 4 out of the 5 tasks they were given.
- These same users believed their code was secure — even when it had serious issues like SQL injection, use of unsafe libraries, or missing authentication in encryption.
- Overconfidence in AI-generated output led to poor decisions. Many just copied the suggestions without reviewing them critically.
- AI rarely applied good security practices on its own, like path canonicalization or the use of prepared statements.
This behavior doesn’t just create immediate security risks — it also builds up technical debt that only shows up later, when the code needs to be maintained, scaled, or audited.
The result:
- More code being written
- Less refactoring
- Inconsistent patterns
- More hidden security and maintenance issues
In other words, more technical debt.
How to measure technical debt
Measuring technical debt has never been easy. Most of the time, we’re talking about things you can’t see — messy structures in the code, shortcuts that were left to fix later, unfinished work. Still, if you can’t measure it, you can’t manage it. Here are a few metrics and indicators that can help:
Technical Debt Index
The Technical Debt Index estimates how much time the team would need to fix all the maintainability issues in the code. Tools like SonarQube calculate this by adding up the estimated time to fix each code smell it finds. For example, one hour to refactor a complex function, thirty minutes to remove duplicate code, and so on. In the end, you get a clear sense of the debt size — something like “we’ve got twelve days’ worth of fixes stacked up.”
Debt Ratio
The Debt Ratio compares the cost of fixing all technical debt with the total cost of developing the software so far. It’s a way to measure how technically “burdened” the project is in relation to its current size.
The formula is simple: divide the estimated effort to pay off the technical debt by the estimated effort to build the system. That effort can be based on code volume, complexity, and other factors.
A Debt Ratio of 0.2 (20 percent) means you’d need to invest 20 percent of the original development effort to clean up the debt. A ratio above 1 (100 percent) means things are so messy it might be cheaper to rewrite the system than fix it.
It’s a helpful metric for getting a strategic view of the project’s health and for checking whether your debt is under control or past the danger zone.
Code duplication
This measures the percentage of code that’s duplicated in different parts of the system. Duplication raises maintenance costs and increases the risk of bugs, since any change needs to be made in multiple places. It’s one of the clearest signs of technical debt.
Test coverage
This shows the percentage of code covered by automated tests. Low coverage usually signals testing debt. The lower the coverage, the higher the risk of regressions and the lower the confidence when refactoring. High coverage doesn’t guarantee quality, but low coverage is almost always a problem.
Method and class size
This measures the number of lines per method or class. Longer blocks of code tend to be less cohesive, harder to understand, and more prone to bugs. It’s a useful metric to spot opportunities for extracting smaller functions, splitting responsibilities, or modularizing.
Technical debt tickets
A common and practical way to track technical debt in agile teams is to log everything directly in the backlog using tickets labeled “Tech Debt” or “Refactor.” These items work like pending technical tasks that need attention but often get pushed aside for product delivery.
From there, you can use the number of tickets or the total story points as a metric for your debt backlog. For example, a team might have 30 technical debt tickets adding up to 60 points. That already gives a more concrete sense of the outstanding work.
But there’s an important catch. This metric only works if the team is disciplined. If no one logs the debt or if the tickets aren’t properly estimated, the data becomes unreliable and not useful at all. It’s a helpful indicator, but it depends heavily on how seriously the team manages its own debt.
Internal quality vs. external quality
Some signs of technical debt don’t show up directly in the code but in what happens around it. That’s why it’s also worth looking at metrics that reflect how quality impacts the final product.
Code churn
Code churn measures how much code gets changed, thrown away, or rewritten in a short period of time. A spike in this number can signal constant rework, poorly planned solutions, or rushed deliveries — all classic signs of technical debt.
This metric becomes even more telling when churn happens right after a merge. That could point to rushed decisions, deadline pressure, or even overreliance on AI-generated code. A recent study showed that code churn is expected to double between 2023 and 2024, reinforcing the idea that more rework is happening in less time.
Defects per KLOC
Another key external metric is the number of bugs or incidents per thousand lines of code (KLOC). When that number is high, it suggests the codebase is prone to errors and instability — which is often a direct result of unresolved technical debt.
If the same bugs keep popping up in the same modules, it’s worth taking a closer look. Is there ignored debt there, or maybe a technical decision that was never properly addressed?
How to handle technical debt
Technical debt isn’t something you can just wipe out from the entire system. And that’s okay. What matters is that it’s intentional, visible, and handled with the right priority when it makes sense. Here are a few practices that work in real life:
- Prioritize by impact and risk
Not all debt needs attention right away. A legacy module that hasn’t been touched in two years can wait. But that critical part of the system where every change breaks something? That should be at the top of your list.
- Build a refactoring routine
Go with the classic scout rule — “leave the code a little better than you found it.” Small, frequent improvements add up over time and help keep debt from piling up again.
- Align with product on technical time
Some teams reserve 10 to 20 percent of each sprint just to tackle debt. Others run dedicated tech sprints every four or five feature sprints. The format doesn’t matter as much — what really counts is having a clear, ongoing agreement between product and engineering.
- Measure the debt and bring the data to the table
Technical debt is also a product metric. In stakeholder meetings, show how it affects bugs, rework, performance, and delivery speed. When the problem becomes a number, it becomes a priority.
- Don’t pay off debt without a clear return
Refactoring just for the sake of it isn’t worth it. Every piece of debt you pay down should unlock something — a new feature, fewer bugs, better performance, or simply more readable code.
Studies show that companies linking technical debt payments to business goals make better decisions and reduce the impact of debt more effectively.
The role of code review in preventing technical debt
One of the most effective ways to keep technical debt out of your codebase is by doing solid code reviews. And that goes way beyond just catching obvious bugs.
Reviews help:
- Avoid duplication and inconsistent logic
- Keep standards and best practices alive across the team
- Highlight spots that might need refactoring later
- Reduce the system’s growing complexity over time
But manual reviews come with two classic problems — time and consistency. When the team is overloaded or the reviewer lacks context, a lot of things slip through the cracks.
This is where AI can actually help a lot.
Kodus, for example, built a code review agent that works directly inside pull requests. Kody watches the comments your team makes on PRs and learns from them. When certain patterns start showing up — like “don’t use new Date() directly” or “this function is too long” — Kody starts suggesting those same comments on its own, in the same style your team usually writes.
On top of that, Kody lets you define custom rules that get automatically checked in the PR. These rules can cover things like:
- Team best practices (for example, dependency injection, date formatting)
- Security standards (like avoiding eval, proper error handling)
- Module consistency (naming, architecture, and structure)
The result?
- Fewer repeated comments
- Faster reviews
- More consistency across PRs
You can try it out in your own repo.
And most importantly: less technical debt slipping into the codebase due to oversight, rush, or lack of context. AI isn’t there to replace the reviewer, but to make sure a baseline of quality and consistency is always maintained — even when the team is stretched thin.
