Flaky failures are one of the most frustrating problems in modern CI environments. A pipeline fails, developers investigate, and nothing meaningful has changed. Rerun the same build, and it passes. Over time, teams start ignoring failures, rerunning pipelines by default, or disabling checks altogether. This erosion of trust is far more damaging than the failures themselves.
Baseline testing offers a practical way to address this problem. Instead of asserting rigid expectations for every test run, baseline testing focuses on detecting meaningful deviations from known-good system behavior. In environments where infrastructure, timing, and external dependencies are inherently unstable, this shift can significantly reduce false failures without lowering quality standards.
Why CI Pipelines Are So Prone to Flakiness?
CI pipelines today operate in highly dynamic environments. Containers spin up and down, services scale automatically, and tests often depend on shared infrastructure or third-party APIs. Even when application code is unchanged, small variations can cause tests to fail.
Common sources of flakiness include:
Network latency and transient timeouts
Non-deterministic ordering of async events
Shared test data across parallel jobs
Dependency version drift
Resource contention in CI runners
Traditional tests assume that the system behaves exactly the same way on every run. In practice, this assumption rarely holds. As a result, many failures signal noise rather than real regressions.
What Makes False Failures So Costly?
False failures slow teams down in subtle but serious ways. Developers lose time investigating issues that do not exist. Release confidence drops because failures no longer reliably indicate risk. Over time, teams start bypassing CI safeguards just to keep delivery moving.
Even worse, real regressions can get missed because they are buried among flaky signals. When everything fails occasionally, nothing feels urgent.
The real problem is not instability alone, but the lack of context around what actually changed.
How Baseline Testing Approaches the Problem Differently?
Baseline testing works by capturing a reference version of system behavior and comparing future executions against that reference. Instead of asking “Did this test produce exactly the expected output?”, baseline testing asks “Did the system behave differently than before in a meaningful way?”
This approach is especially effective in CI environments because it tolerates acceptable variability while still highlighting true behavioral drift.
A baseline can include:
API request and response patterns
Execution timing ranges rather than fixed values
Interaction sequences across services
Side effects such as database writes or emitted events
By comparing behavior holistically, baseline testing avoids failing builds over insignificant differences.
Filtering Noise Without Ignoring Real Problems
One of the biggest strengths of baseline testing is its ability to distinguish noise from signal. For example, response times might vary slightly between CI runs due to shared infrastructure. A traditional test might fail if a response exceeds a strict threshold. Baseline testing, on the other hand, can detect whether the response time is still within an expected range compared to previous runs.
Similarly, when responses include dynamic fields like timestamps or IDs, baseline testing can ignore or normalize those fields while still validating the overall structure and semantics of the response.
This selective comparison dramatically reduces false failures without hiding genuine regressions.
Baseline Testing and Flaky Integration Points
Flakiness often originates at integration boundaries rather than in business logic. APIs, message queues, caches, and external services behave differently under load or during transient failures.
Baseline testing is particularly effective here because it validates interaction patterns instead of individual assertions. If an API call occasionally returns responses in a different order but the overall contract remains intact, baseline testing will not fail the build unnecessarily.
When a real issue appears, such as a missing field or unexpected side effect, the baseline comparison surfaces it clearly.
Making CI Failures Actionable Again
A CI failure should answer one question clearly: “What changed?” Baseline testing helps restore that clarity.
Instead of cryptic assertion errors, developers can see exactly how behavior diverged from the baseline. This context makes failures faster to diagnose and easier to trust. Over time, teams stop rerunning pipelines blindly and start treating failures as meaningful signals again.
Tools like Keploy, for example, apply baseline testing concepts to API and system-level testing by capturing real traffic and comparing behavior over time. This allows teams to validate changes without writing brittle assertions that break on harmless variation.
Where Baseline Testing Fits Best in CI Pipelines?
Baseline testing is not meant to replace all other testing techniques. Unit tests and deterministic checks are still essential for validating logic at a granular level. Baseline testing works best when applied to:
Integration and system tests
API behavior validation
Microservices communication flows
End-to-end workflows with multiple dependencies
In these areas, strict assertions tend to create more noise than value. Baseline testing adds resilience where it is needed most.
Avoiding Common Pitfalls
Baseline testing must be used thoughtfully. Poorly designed baselines can hide regressions if they are updated too frequently or capture incorrect behavior.
To avoid this:
Only update baselines after deliberate validation
Review baseline diffs during code review
Exclude truly volatile data from comparisons
Combine baseline testing with targeted assertions
When treated as a controlled reference rather than an automatic snapshot, baseline testing strengthens CI reliability instead of weakening it.
Why Baseline Testing Is Gaining Momentum?
As systems grow more distributed and CI environments become more dynamic, the limitations of rigid testing become more apparent. Teams need feedback that reflects real system behavior, not idealized conditions.
Baseline testing aligns better with how modern software actually runs. By focusing on meaningful change instead of perfect consistency, it reduces false failures, improves developer confidence, and keeps CI pipelines moving without sacrificing quality.
In flaky environments, the goal is not to eliminate variability, but to understand it. Baseline testing gives teams that understanding—and turns CI failures back into signals worth acting on.
