Answer.

The problem of technical debt in automated tests was first realized with the growth of automation — when the number of tests reached into the hundreds and thousands, their maintenance often cost more than the development itself, and architectural mistakes multiplied.

Background

In the early days of automation, tests were written quickly, often without patterns, standards, and subsequent refactoring. As a result, automated test repositories become outdated, break with application changes, and their maintenance requires increasing effort.

Problem

Rapid writing "on the spot" creates a chaotic test structure.
Lack of refactoring leads to duplication and poor readability.
Low involvement of developers in automated tests.
Outdated test scenarios that do not reflect current product requirements.

Solution

Implementing regular test refactoring practices — code review, linting, formatting standards, architectural patterns.
Reducing duplication — PageObject, Factory, Service Layer, and other patterns.
Constant updating of test scenarios together with the product team.
Using tools for automatic coverage analysis and obsolete code.

Key features:

Regular Test Refactoring Cycle
Mandatory Code Review of automated tests
Collaboration between QA and development

Trick Questions.

Is a high code coverage percentage an indicator of the absence of technical debt?

No, formal code coverage does not guarantee the quality and viability of the test base: there may be outdated or "unnecessary" tests.

Is it enough to write templates for automated tests once to eliminate technical debt?

No, infrastructure and patterns always require review and development as the project grows.

Can we completely do without manual testing if automated tests are well-structured?

No, smoke/regression/niche tests will always be needed manually, and automated tests are essential for regular "monitoring" of stable functionality.

Typical Mistakes and Anti-Patterns

Lack of refactoring
Excessive nesting and complexity of tests
Interrupting CI pipelines due to unstable old tests

Example from Real Life

Negative Case

Automated tests were written without review, the structure changed during the project, some tests became outdated — 40% of tests failed due to changes in the application.

Pros:

Quickly achieving "large" coverage in 2-3 months

Cons:

Huge time costs for maintenance
Mass false failures

Positive Case

In the team, test reviews and refactoring are conducted every two weeks, and the architecture is maintained according to accepted standards, tests are closely tied to relevant user stories.