Solving the Technical Debt Challenge: Key Strategies by Jasiri Limited

Technical Debt Isn’t a Problem – Until It Becomes the Product

Every product accumulates technical debt. It’s not a sign of failure – it’s a sign of progress under constraints.

Deadlines happen. Requirements change. MVPs turn into long-term platforms. Teams grow. Integrations pile up. “Quick fixes” become permanent. All of it is normal.

The real issue is not technical debt existing.
The issue is technical debt becoming invisible.

When debt is ignored, it turns into a silent tax on the business:

• delivery slows down
• regressions increase
• incident rates grow
• teams stop trusting the system
• scalability becomes expensive and risky

Solving technical debt is less about “cleaning code” – and more about restoring engineering effectiveness.

The Debt Triangle: Where Technical Debt Actually Comes From

Most teams assume debt comes from “bad code.” In reality, technical debt usually comes from the intersection of three forces:

Speed pressure

Shipping fast is necessary early on, but shortcuts become embedded.

Complexity growth

As systems expand, complexity compounds. The same patterns that worked at 10k users fail at 1M.

Ownership gaps

When code has no clear owner (team rotations, restructuring), debt grows automatically.

Debt is rarely created by one bad decision. It is created by many small decisions that were never revisited.

Step One: Stop Treating Debt as a Feeling

Many teams talk about technical debt like this:

• “This code is messy.”
• “The system is hard to work with.”
• “We should refactor at some point.”

That language makes debt impossible to manage.

A healthier approach is to treat debt like any other product risk:

• visible
• trackable
• measurable
• prioritized

Instead of asking:

“Is this debt bad?”

Ask:

“What does this debt cost us – per sprint?”

Debt becomes manageable when it’s quantified.

Debt Discovery: How to Identify Technical Debt Without Guessing

You can’t solve what you can’t see. The most effective teams identify debt using a combination of engineering signals and business signals.

Engineering signals

• increasing PR review time
• frequent regressions
• flaky tests
• slow builds / slow deployments
• growing dependency conflicts
• duplicated logic across services
• “scary areas” engineers avoid touching

Business signals

• missed deadlines despite high workload
• rising incident frequency
• longer onboarding time for new engineers
• feature requests blocked by “tech limitations”
• inability to scale performance without cost spikes

Technical debt is often already visible – just not labeled.

The Prioritization Model: Not All Debt Is Worth Paying

A common mistake is trying to “clean everything.” That doesn’t work because:

• time is limited
• the business still needs features
• not all debt creates meaningful harm

A better approach is to rank debt using ROI thinking.

Use a simple prioritization filter

Rate each technical debt item by:

1) Risk

• can it break production?
• does it create security issues?
• does it increase compliance exposure?

2) Speed impact

• how much does it slow development?
• how many teams are blocked by it?

3) Scalability impact

• will it collapse under growth?
• does it prevent modular expansion?

4) User impact

• does it create bugs, downtime, poor UX?

Debt that scores high on these criteria is strategic debt – it must be paid down.

A Practical Rule: Attack “Debt Hotspots,” Not the Whole Codebase

Most debt isn’t evenly distributed. It clusters.

Debt hotspots include:

• core authentication flows
• billing and payment logic
• onboarding and permissions
• data pipelines
• legacy integration modules
• overloaded “god services”
• the area where most incidents originate

Instead of refactoring everything, focus on the 10–20% of the system that creates:

• 80% of failures
• 80% of complexity
• 80% of slowed delivery

This is the fastest path to visible improvement.

The Technical Debt Toolkit: What Actually Works in Real Teams

Below are strategies that consistently work across different architectures and team sizes.

Strategy 1 – Build a Debt Register

Debt becomes manageable when tracked.

A strong debt register includes:

• debt title + location (service/module)
• impact summary (business + engineering)
• priority score (risk/speed/scalability)
• recommended fix level (quick fix vs redesign)
• owner team
• planned quarter

Without a debt register, debt only gets attention when incidents happen.

Strategy 2 – Define Debt Categories

Debt is not always code-level. It includes:

• code debt (design flaws, duplication, architecture mismatch)
• test debt (missing coverage, slow pipelines, flaky suite)
• infra debt (manual deployment, weak monitoring)
• data debt (bad schema, inconsistent analytics, missing logs)
• documentation debt (tribal knowledge)
• dependency debt (outdated libraries, security risks)

Categorization prevents teams from solving only the visible problems.

Strategy 3 – Create a Debt Budget

The most sustainable teams allocate capacity continuously.

Examples:

• 15–25% of sprint capacity reserved for debt
• one debt sprint per quarter
• rotation model: one engineer per sprint focuses on debt
• “fix as you touch” rule: improve while working in affected code

If debt work depends on “free time,” it will never happen.

Strategy 4 – Introduce Guardrails (So Debt Doesn’t Return)

Paying debt without guardrails creates a cycle of repeat debt.

Guardrails include:

• code review standards
• architectural decision records (ADRs)
• consistent conventions
• shared libraries
• linting rules and static checks
• CI quality gates
• minimum testing requirements
• deprecation policy

Debt must be prevented, not just resolved.

Strategy 5 – Refactor Through Iterative Redesign

Big rewrites fail often. The risk is too high.

A healthier approach:

• isolate the problematic area
• introduce a new version (v2 module)
• migrate gradually
• remove legacy paths over time

This approach keeps delivery moving while reducing debt safely.

The Fast Wins: Debt Improvements That Show Impact Quickly

Not every debt fix is huge. Some have immediate impact:

• improving logging and tracing
• reducing flaky tests
• standardizing error handling
• introducing API contracts
• improving CI build times
• adding missing monitoring dashboards
• simplifying configuration management
• removing dead code and unused dependencies

These wins increase team confidence and build momentum for larger refactors.

Technical Debt Is a Leadership Problem Too

Technical debt isn’t solved only by engineers.

It requires leadership agreement on one thing:

long-term delivery speed requires short-term investment.

When leadership sees technical debt as “not urgent,” the system slowly becomes:

• slower
• riskier
• harder to scale
• more expensive to change

Debt becomes business debt.

The most successful teams treat debt reduction as:

• operational resilience
• product stability
• scalability investment
• risk management

Final Thought: A Healthy Codebase Is a Competitive Advantage

Technical debt is unavoidable – but unmanaged debt destroys the core advantage of modern companies: speed.

A healthier codebase enables:

• faster feature delivery
• stable releases
• easier onboarding of new engineers
• better scalability
• reduced production risk

Solving technical debt isn’t about perfection.
It’s about building a system that can grow without collapsing under its own complexity.