[Tool] Invoke task to check and format missing replace dependency in go.mod files

[Pythyu]

What does this PR do?

Add the invoke task inv internal-deps-checker to check that every go.mod file has it's internal required dependencies added into replace. The option --formatFile allow to format go.mod files that are missing replace dependencies.

Motivation

When go mod download is running, it will stop at the first dependency unreplaced and won't show all missing ones in a single run.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Reviewer's Checklist

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• [ ] Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
• [ ] A release note has been added or the changelog/no-changelog label has been applied.
• [ ] Changed code has automated tests for its functionality.
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• [ ] If applicable, the config template has been updated.

[KevinFairise2]

I agree that the go.mod files are a bit a mess, however in some cases the different require section can be useful, for example the direct and indirect require are not mixed. If I understand correctly running your formatter would mix the direct and indirect import, right?

[Pythyu]

By default it's only going to check if there's some missing replace dependency and format when --formatFile is activated. From go point of view it doesn't change anything if indirect and direct are in the same require or multiple but it's a fair point of improvement in presentation. The reason why we have this drawback is that by default when the modfile go module AddReplace its adding an extra line replace <dep> <vers>. This needs to be merged to avoid having replace everywhere in any order.

I'll modify the formatter to try to merge it into two different require one for indirect and one for direct to keep current go.mod format 👍

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: d1138e76-f395-4f21-87ad-aa770cc60713 Baseline: 7fad488caacff09e4cc911c07f064f2ee33d5f3c Comparison: f2a08a8f59b2eb299986bd3b17573abeda33ce3f Total CPUs: 7

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

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	-0.32	[-6.85, +6.22]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+4.70	[+3.24, +6.16]
➖	tcp_syslog_to_blackhole	ingress throughput	+0.90	[+0.85, +0.96]
➖	idle	memory utilization	+0.47	[+0.43, +0.51]
➖	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_msgpack	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.00	[-0.04, +0.04]
➖	process_agent_standard_check	memory utilization	-0.08	[-0.14, -0.02]
➖	process_agent_real_time_mode	memory utilization	-0.15	[-0.20, -0.11]
➖	process_agent_standard_check_with_stats	memory utilization	-0.31	[-0.36, -0.26]
➖	file_to_blackhole	% cpu utilization	-0.32	[-6.85, +6.22]
➖	file_tree	memory utilization	-0.32	[-0.43, -0.22]
➖	otel_to_otel_logs	ingress throughput	-0.86	[-1.48, -0.24]

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".

[Pythyu]

/merge

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