Does Your Technical Debt Have a Clear Price Tag?

Like most CTOs and CIOs you know where the weak parts of your codebase are (repos that take longer to change, places where small requests balloon into major projects). But, also like most leaders, you might find it harder to see what those problems are costing you.

Technical debt doesn’t usually arrive on a finance report with its own line item. Instead, the cost is absorbed elsewhere: engineering hours, recovery time, complicated changes, extra review. 

While it’s easy to recognize, it’s much harder to prioritize. 

Technical debt is already showing up in your operating costs

Knowing where you’re burning engineering capacity dealing with iffy code is essential if you’re going to get a grip on technical debt costs.

Sometimes that means identifying a developer spending longer understanding an unfamiliar part of the codebase. Sometimes it means seeing a pull request that’s been sitting open while people work out the consequences of a change. Sometimes it’s as obvious as production breaking completely. 

Our research found a striking difference in incident recovery depending on the maintainability of the code involved.

For incidents affecting the least maintainable quarter of code, median recovery PR duration was 65.2 hours. For the most maintainable code, it was just 1.7 hours.

That’s a 38-fold observed difference.

There are plenty of variables involved in incident recovery, so that figure doesn’t mean it was only poor maintainability that caused the delay. But it does show why technical debt deserves more than a place on an engineering backlog.

An incident can consume far more engineering time than the outage suggests

When an incident involves difficult code, the business might end up carrying much more recovery effort and operational exposure; and recovery time is only part of the cost.

There’s also the engineering effort involved in diagnosing the problem, making the change, reviewing it, testing it and getting the system stable again.

Our analysis found that this effort rose sharply as incident fixes got more complex.

For incident fixes involving more than 40 commits, median coding effort reached 129.4 hours, compared with 37.62 hours for fixes involving 21–40 commits.

That matters when you’re discussing technical debt with Finance.

An outage might last six hours; the engineering cost attached to it can extend far beyond those six hours.

Multiply that across a large estate and technical debt starts consuming capacity that could have gone into new products, customer work, security improvements or the roadmap.

The challenge is putting a credible number against it.

Start with engineering capacity

There’s a temptation to jump straight to a big financial figure.

“Our technical debt costs £10 million a year” sounds compelling. But it also opens up the obvious question: How did you calculate that?

A stronger business case starts closer to your engineering data.

First, identify clusters of maintainability problems. Which repos or areas of code are consistently harder to change?

Then look at the engineering effort associated with them. Prioritising tech debt tasks by effort and impact is crucial when engineering resources are stretched. The ideal is having an objective metric to quantify the before and after so you can see: How much time is being absorbed by remediation? How long do changes take? How much developer capacity goes into dealing with bugs instead of delivering planned work?

From there, you can convert that effort into capacity.

If maintaining a particular part of the estate is consuming 4,000 additional engineering hours a year, that gives you something concrete to work with.

You can then apply your organization’s own loaded engineering costs, supplier rates or other financial assumptions. The resulting number is far easier to defend because you can trace it back to what is happening in the codebase.

Some technical debt costs much more than the rest

A single “technical debt” figure can still be misleading: a decade-old rarely-changed system may carry plenty of debt without creating much day-to-day business impact, whereas another application could look healthier but be directly in the path of regular releases or customer transactions. 

The second one may deserve investment first.

Our maintainability research found several historical signals associated with increased incident risk, including prolonged PR review cycles, repository-level maintainability deficit and file age. The strongest association in that analysis was prolonged PR Time to Close, at 1.34×.

It doesn’t make PR duration a cause of incidents, but it does suggest that CTOs should more usefully be looking at where technical debt is creating enough engineering cost or operational exposure to warrant action.

That turns debt reduction into a prioritization problem. Which is much easier to fund.

AI raises the stakes

AI-assisted development adds another reason to get better at this. Code can now be produced and changed faster, which can deliver real productivity gains.

But if you’re only measuring output, you can miss what’s simultaneously happening to code quality.

Our research into the GenAI period found productivity rising while maintainability declined. The report recommends measuring maintainability alongside delivery output when assessing the effect of AI-assisted development.

If AI is helping engineers move faster, you need to know if your software estate is becoming easier or harder to work with. Then you can go to Finance with a clear number, showing exactly which areas need addressing. 

Give them answers to these questions and you’ll build a stronger business case for rollout requests and deployment strategies, and avoid spending on debt that has minimal material impact while overlooking higher-cost problems:

• Where did the figure come from?

• Which systems are responsible?

• How much engineering time is involved?

• What assumptions have you used?

• What would change if we fund the work?

• What happens if we don’t?

Put a price on the problem before you ask for the budget

Once you can connect maintainability to engineering effort, recovery time and capacity, you have a much more useful basis for deciding what to fix.

You can compare the cost of leaving a problem in place with the likely value of addressing it, and prioritize the areas that matter most.

And when Finance asks why technical debt deserves investment, you have an answer grounded in your own engineering data rather than an estimate pulled from a slide.

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Want to track where poor maintainability is consuming engineering capacity in your software estate?

Book a no-strings consultation with our team to see your maintainability cost, along with more engineering insights. 

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