Answer.

Historically, as the number of automated tests in projects increased, issues arose: tests became confusing, exceeded execution time limits, and it was hard to understand what was responsible for what. Moreover, the risk of dependencies between different parts of the testing system increased, slowing down the overall pipeline performance.

The problem arises when the number of tests grows faster than the architectural support for the testing infrastructure can accommodate. Without scalable solutions, tests become slow, difficult to maintain, complicating the search and localization of defects, and technical debt rapidly increases.

The solution lies in implementing specific strategies:

Clustering tests by modules and levels (unit, integration, E2E) using appropriate tags and filters.
Parallel test execution (sharding, distributed test suites) to speed up execution.
Using a microservices approach in the testing infrastructure: standard DSL abstractions, separate services for managing test infrastructure.
Automating the detection of duplicate and outdated tests, regular refactoring, and coverage auditing.

Key features:

Modularity and reusability of tests and test libraries.
Full automation of CI/CD integrations and the ability for resource autoscaling.
Implementation of monitoring tools for the quality of automated tests and code coverage.

Tricky questions.

Can all tests be made integration tests to cover more code at once?

No, this approach reduces defect localization and leads to high maintenance costs, as well as slowing down regression execution.

Does scalability of automated tests mean only their acceleration?

Scalability encompasses architecture, maintainability, acceleration, and flexible infrastructure. Acceleration is merely a consequence of a well-designed large system.

How to correctly scale tests for teams working across different time zones?

It is important to provide the possibility of local execution and independence of testing environments; otherwise, there will be "conflicts" between team tasks.

Typical mistakes and anti-patterns

All tests are written in one directory without structuring by domains.
Directly reusing test data (“copy-paste” instead of libraries/fixtures).
Lack of monitoring/metrics on execution time and test stability.

Real-life example

Negative case

Several teams in the company started adding new automated tests to one folder without coordinating their changes. After a few weeks, the automated tests began failing due to mismatches in data and dependencies, and the execution time exceeded 2 hours.

Pros:

Low entry threshold for beginners.
Quick start of automation.

Cons:

Lack of scalability.
Difficulty in searching and analyzing errors.
Slowing down product releases.

Positive case

In one of the teams, a modular structure was created, separate CI was introduced by code areas, stability improved, and automatic alerts about ineffective tests were implemented.