Technical Debt in App Development: What It Is, Causes, and Management Methods

What Is Technical Debt in App Development

Technical debt is a concept introduced by Ward Cunningham in 1992 to describe the gap between the current state of code and the ideal architecture. The term draws an analogy with financial debt: if you take out a technical loan (choose a quick solution), the interest (maintenance complexity) accumulates over time.

Unlike bugs, technical debt is not a logic error — it is an architectural compromise that speeds up current development but slows down future development. For example, copying a code fragment instead of extracting a common function speeds up implementation by an hour but adds weeks of maintenance when requirements change.

According to McKinsey (2025), companies with high levels of technical debt spend 20–40% more resources on implementing new features compared to competitors. This makes debt management not a technical option but a business necessity.

Main Causes of Technical Debt

Tight deadlines — the most common cause. The team chooses to do it quickly, rewrite later, but later never comes. Production releases accumulate compromises, and the system gradually loses architectural integrity.

Lack of code review leads to suboptimal solutions entering the main branch without discussion. A SmartBear (2024) study shows that projects without mandatory review accumulate technical debt 2.3 times faster than those practicing pair programming or formal code inspections.

Changing requirements — another source. An architecture designed for one set of business conditions breaks when the context changes. Developers build new layers on top of old logic instead of redesigning, leading to increased cyclomatic complexity.

Insufficient testing makes refactoring risky. The team is afraid to rewrite code because it is unclear which scenarios will break. A vicious cycle: without tests you cannot safely refactor, without refactoring you cannot add tests.

Types of Technical Debt: Strategic and Unintentional

Strategic technical debt is a conscious choice by the team to postpone architectural improvements for the sake of a quick launch. MVP products, prototypes, and A/B tests are classic examples. Such debt is planned and repaid after the hypothesis is validated.

Unintentional technical debt arises from a lack of knowledge of best practices, absence of architectural vision, or poor team communication. It is not planned, not estimated, and accumulates uncontrollably. According to ThoughtWorks (2024), unintentional debt accounts for 60–70% of all technical debt in a typical project.

Architectural technical debt — outdated patterns and anti-patterns like God Object or Spaghetti Code. Testing technical debt — lack of unit tests, integration tests, and UI tests. Infrastructure technical debt — manual deployments, lack of CI/CD, outdated tool versions.

How to Measure Technical Debt in a Project

Time to implement — a key metric. If adding a simple feature takes several days instead of hours, technical debt is high. SonarQube provides a quantitative assessment through the Debt Ratio indicator: the ratio of time to fix all identified issues to the total development time.

Cyclomatic complexity — a metric showing the number of independent paths in the code. Normal complexity is up to 10 per function. Values above 25 indicate serious architectural debt. Tools like CodeClimate and NDepend automatically track this metric in the repository.

Technical coefficient — the ratio of lines of code added during refactoring to lines added when creating new functionality. A coefficient below 0.1 indicates that the team is not paying attention to code quality.

Incident frequency — an indirect indicator. An increase in the number of bugs after releases without changing the scope of functionality indicates debt accumulation. Monitoring through Sentry or Crashlytics helps track this trend over the long term.

Technical Debt Management Strategies

Technical debt backlog — a dedicated list of refactoring and code improvement tasks. Each task is assessed for complexity and impact on development speed. It is recommended to allocate 20–30% of each sprint to tasks from this backlog, as advised by Martin Fowler (2024) in his recommendations on technical debt management for agile teams.

The Boy Scout Rule — leave the code cleaner than you found it. Every change to legacy code should be accompanied by micro-refactoring: renaming a variable, extracting a method, adding a test. The cumulative effect of such micro-improvements significantly reduces debt over 6–12 months.

Quadrant analysis — classification of technical debt along two axes: importance and urgency. Critical debt (Reckless + Prudent according to Fowler’s classification) requires immediate resolution. Non-critical debt is planned for the backlog. RCA (Root Cause Analysis) for each critical case prevents recurrence of the problem.

Refactoring Methods and Debt Repayment

Strangler Fig pattern — gradual replacement of system modules without stopping the product. The new module is deployed alongside the old one, and traffic is gradually switched over. The pattern is especially effective for microservice architecture, where each service can be replaced independently.

Big Rewrite — a complete system overhaul from scratch. The riskiest approach: according to Standish Group (2024), 75% of full rewrite projects exceed the budget or miss deadlines. Apply only when technical debt blocks any development and maintenance costs exceed rewrite costs.

Test coverage — the foundation of safe refactoring. Before changing legacy code, add characterization tests that capture the current behavior. Then refactor under the protection of these tests. According to Michael Feathers (2023), this approach reduces the risk of introducing bugs during refactoring by 70%.

Example: Refactoring via Method Extraction

def processOrder(order) {
    // Before: 60 lines with validation,
    // discount calculation and email sending
}

def validateOrder(order) { /* extracted */ }
def calculateDiscount(order) { /* extracted */ }
def sendConfirmation(order) { /* extracted */ }