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Regression Detector Results

Run ID: 77b4b4c6-f8e6-4744-944a-326dd88ff451 Baseline: b061cc85aa957a0e173337bebdc913d4903fe6d5 Comparison: 7d1ffd2fa3fe51a717c409e9fe8e2bcdb383bc6a

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

Experiments with missing or malformed data

• basic_py_check

Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.23	[-6.32, +6.78]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.76	[-0.66, +2.18]
➖	file_tree	memory utilization	+0.59	[+0.51, +0.68]
➖	process_agent_real_time_mode	memory utilization	+0.56	[+0.52, +0.61]
➖	tcp_syslog_to_blackhole	ingress throughput	+0.44	[+0.38, +0.50]
➖	otel_to_otel_logs	ingress throughput	+0.44	[-0.19, +1.07]
➖	file_to_blackhole	% cpu utilization	+0.23	[-6.32, +6.78]
➖	process_agent_standard_check	memory utilization	+0.21	[+0.18, +0.25]
➖	trace_agent_msgpack	ingress throughput	+0.03	[+0.02, +0.04]
➖	process_agent_standard_check_with_stats	memory utilization	+0.02	[-0.03, +0.06]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.01	[-0.03, +0.02]
➖	idle	memory utilization	-0.15	[-0.19, -0.11]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".