CODE TUNER INSIGHTS

Is Your Technical Debt Widespread or Concentrated?

Technical debt is rarely distributed evenly. Learn how to distinguish a widespread problem from one expensive part of the codebase.

When software becomes difficult to work with, “we have too much technical debt” can sound like a diagnosis. It is usually the beginning of one.

A product may look unhealthy overall while most delivery friction comes from a single component. Equally, one troublesome area can distract attention from deterioration across the wider estate. The difference determines where to invest and how much feature work needs to pause.

When the problem is concentrated

Imagine a city with one busy district that is difficult to navigate and expensive to maintain. Renovating every neighbourhood would be a poor response to a local problem.

Software can behave in the same way. Signs of concentration include:

• One component with much higher complexity than comparable areas.
• A customer-facing subsystem that repeatedly appears in difficult features.
• A repository absorbing a disproportionate share of maintenance effort.
• One area deteriorating while the rest of the product remains stable.

A targeted intervention may be enough. Ring-fence the affected work, identify the outcomes it should improve and allow healthy parts of the product to keep moving.

When the problem is widespread

A broader response becomes more plausible when changes are becoming harder across multiple parts of the product. Several components show rising complexity, comparable work becomes less predictable and maintenance demand spreads between teams.

This may indicate that the overall cost of change is increasing. A larger, time-bounded investment can then have a stronger case than a series of isolated fixes.

Check alternative explanations first. A common testing bottleneck, unclear requirements or new coordination demands can affect several teams without implying that the whole codebase needs remediation.

Why the distinction changes the investment case

Broad technical-debt programmes consume substantial capacity. In a hypothetical product where a small part of the codebase causes most delivery friction, spreading the budget evenly would spend money far from the problem.

McKinsey describes an insurance company that prioritised technical debt around valuable user journeys and the systems supporting them. The relevant principle is to connect intervention to business value rather than treat the technology estate uniformly.

A local problem calls for a local business case: which work becomes easier after this component improves? A widespread problem needs a broader case: how will the intervention protect the organisation’s ability to change its software?

Five questions to separate the two

Is deterioration visible in several components?

Look at trends over a consistent period. A single aggregate score can conceal both healthy areas and serious outliers.

Does the same subsystem recur in delayed work?

Repeated connections between one component and difficult delivery suggest concentration. Check the actual work involved rather than relying only on reputation.

Are several teams experiencing similar friction?

That strengthens the case for a shared problem, although the cause may still sit in a common dependency or delivery process.

Do healthy areas continue to deliver normally?

If they do, avoid slowing them automatically because another part of the estate needs attention.

What would change your diagnosis?

Identify the evidence that would move you from a targeted response to a broader programme, or the reverse. This makes the scope open to challenge.

Keep the response proportional

Target a deteriorating component when the evidence points to concentration. Consider a wider intervention when several parts of the system are genuinely affected. Where the evidence remains unclear, investigate and set a review point.

The useful question is where technical conditions are affecting the ability to build. Answer that before increasing maintenance everywhere.

Make the next engineering decision with better evidence.

Code Tuner analyses technical debt, complexity and codebase health to support engineering judgement and investment decisions. Explore how Code Tuner can help your team.