01 · CAPABILITY
Establish a known-good baseline
Keep a successful run with a controlled scenario, traffic model, environment, and thresholds as the comparison point for future releases.
PERFORMANCE REGRESSION TESTING
Know when a release gets slower before users notice.
Turn repeatable load tests into a performance regression signal. Compare releases against a stable baseline, enforce latency and error budgets, and notify the team when performance drifts even if the application still “works.”
FREE TO START · No credit card · HTTP performance testing · Managed execution
TEST DESIGN
Regression testing is a comparison problem
01Baseline
Pin the environment and workload before comparing builds.
02Delta
Track the size and direction of latency/throughput changes.
03Noise floor
Ignore fluctuations smaller than your repeatability envelope.
WHY THIS PAGE EXISTS
Performance is a trend, not a one-time benchmark.
Many regressions are too small to cause an outage on the day they ship. A query adds 40 ms, a cache hit rate slips, an extra downstream call appears, and months later the system has lost most of its headroom. Regression testing catches that drift by repeating the same workload and comparing today’s result with a trusted reference.
02 · CAPABILITY
Measure relative and absolute failure
A run can violate an absolute p95 budget, regress meaningfully versus baseline, or both. Track both signals so slow drift does not hide below a generous ceiling.
03 · CAPABILITY
Use stable workloads
Regression detection depends on repeatability. Keep request mix, test data, pacing, duration, and load profile as consistent as practical.
04 · CAPABILITY
Connect results to releases
Run checks after deployments, before release promotion, or on a schedule so the team can associate a change in performance with a change in the system.
05 · CAPABILITY
Alert on meaningful drift
Use regression notifications, Slack or email alerts, and shared result links so a performance change becomes visible to the people who can act on it.
06 · CAPABILITY
Preserve capacity over time
Catching small regressions early protects future headroom and reduces the chance that several individually acceptable changes combine into a larger performance problem.
WORKFLOW
Compare releases without confusing noise for a regression.
A regression comparison is only credible when the setup stays controlled. Preserve the scenario, environment class, dataset, warm-up, and measurement window so the measured delta belongs to the build.
01
Choose a representative baseline
Use a run from a stable environment and a known-good release. Record the scenario, load profile, and relevant system conditions.
02
Define the regression budget
Set absolute thresholds and decide what relative change is meaningful enough to investigate.
03
Repeat under comparable conditions
Use the same workload and target environment class. Large environment differences can overwhelm the code-level signal you are trying to measure.
04
Compare before promoting
Review current latency, failures, throughput, and run-to-run deltas before a risky release reaches more users.
05
Keep the baseline intentional
Update the baseline when the system has materially improved or the workload has legitimately changed. Do not silently normalize a regression by replacing the baseline after every run.
PRODUCT FIT
Release-to-release performance checks with the same test workflow
LoadTester combines repeatable test definitions, run comparison, thresholds, scheduled tests, CI/CD integration, and regression notifications so teams can move from occasional benchmarking to continuous performance awareness.
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IMPLEMENTATION GUIDE
Want the regression testing playbook?
Read the release regression guide for baselines, comparison rules, thresholds, and rollout patterns.
Open the supporting guide →OTHER SOLUTIONS
Choose the comparison workflow that matches the release risk.
FAQ
Regression-testing questions that determine whether a delta is real.
What is performance regression testing?
It is the practice of repeating a representative performance test and comparing the result against a previous known-good state. The goal is to detect slower latency, higher errors, lower throughput, or lost capacity introduced by change.
What should be used as a baseline?
Use a stable run from a known-good release under comparable environment and workload conditions. The baseline should be intentional and documented, not simply the most recent run.
How large should a regression be before failing a release?
There is no universal percentage. Choose a limit based on measurement noise, user impact, and available performance headroom. Pair relative regression limits with absolute latency and error budgets.
Can regression testing be scheduled?
Yes. Scheduled tests are useful for detecting drift even when no single deployment is suspected. CI/CD checks are useful for associating a regression closely with a release.
Compare the release before users become the regression detector.
Establish the first repeatable baseline on the free plan, then automate the same comparison once the team knows which deltas are actionable.