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7a658b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | """TimingFlakeGenerator — scenario family: timing_flake."""
from __future__ import annotations
import random
from ci_triage_env.data.clustering.archetypes import Archetype
from ci_triage_env.data.generators._helpers import (
ArchetypedGenerator,
_metric_samples,
build_base_outputs,
fake_short_sha,
fake_timestamp,
fill_template,
make_failure_summary,
pick_test_name,
scenario_id_for,
)
from ci_triage_env.schemas.diagnosis import DiagnosisLabel
from ci_triage_env.schemas.scenario import (
GroundTruth,
Scenario,
ScenarioMetadata,
TerminalActionSpec,
ToolOutput,
)
class TimingFlakeGenerator(ArchetypedGenerator):
family_name = "timing_flake"
label = DiagnosisLabel.TIMING_FLAKE
def informative_tools(self) -> list[str]:
return ["read_logs", "query_flake_history", "cluster_metrics"]
def minimal_evidence_set(self) -> list[str]:
return ["query_flake_history", "cluster_metrics"]
def _default_archetypes(self) -> list[Archetype]:
return [
Archetype(
archetype_id="timing_flake_001",
family="timing_flake",
pattern_summary="Test timeout: deadline exceeded after N seconds",
log_template=(
"--- FAIL: {TEST_FUNC} ({DURATION}s)\n"
" {TEST_MODULE}_test.go:{NUM}: context canceled: deadline exceeded\n"
"FAIL\t{TEST_MODULE}\t{DURATION}s\n"
"panic: test timed out after {TIMEOUT}s"
),
slot_distributions={
"TEST_FUNC": ["TestAPIResponse", "TestDBQuery", "TestCacheLoad"],
"TEST_MODULE": ["github.com/org/repo/api", "github.com/org/repo/storage"],
"DURATION": ["30.001", "60.002", "120.000"],
"TIMEOUT": ["30", "60", "120"],
"NUM": ["47", "83", "124"],
},
informative_tools_hint=["read_logs:full", "query_flake_history", "cluster_metrics:queue_depth"],
minimal_evidence_hint=["query_flake_history", "cluster_metrics:queue_depth"],
),
Archetype(
archetype_id="timing_flake_002",
family="timing_flake",
pattern_summary="Context canceled / timeout exceeded in async call",
log_template=(
"FAILED {TEST_MODULE}::{TEST_FUNC} - TimeoutError\n"
" TimeoutError: Operation timed out after {DURATION} seconds\n"
" During handling of the above exception:\n"
" asyncio.exceptions.TimeoutError\n"
"short test summary info\n"
"FAILED {TEST_MODULE}::{TEST_FUNC} — timeout exceeded"
),
slot_distributions={
"TEST_FUNC": ["test_async_call", "test_rpc_response", "test_batch_process"],
"TEST_MODULE": ["tests/integration/test_rpc", "tests/integration/test_batch"],
"DURATION": ["5.0", "10.0", "30.0"],
},
informative_tools_hint=["read_logs:full", "query_flake_history", "cluster_metrics:queue_depth"],
minimal_evidence_hint=["cluster_metrics:queue_depth"],
),
]
def generate(self, seed: int, source_log_hash: str | None = None) -> Scenario:
rng = random.Random(seed)
archetype = self._pick_archetype(rng)
log_text = fill_template(archetype.log_template, archetype.slot_distributions, rng)
test_name = pick_test_name(rng)
summary = make_failure_summary(
self.family_name, rng, test_name=test_name, log_excerpt=log_text
)
branch = summary.branch
rerun_passes = (seed % 2 == 0) # passes ~50% of time
outputs = build_base_outputs(
test_name, branch, rng,
log_lines=log_text.splitlines(),
rerun_passes=rerun_passes,
)
# --- informative overrides ---
# queue_depth elevated — CI under load → timeouts
outputs["cluster_metrics:queue_depth"] = ToolOutput(
tool_name="cluster_metrics",
payload={"samples": _metric_samples(rng, "queue_depth", elevated=True, n=6)},
cost_units=0.003,
)
# Flake history: intermittent, correlates with CI load spikes
total_runs = 30
failures = rng.randint(5, 12)
outputs[f"query_flake_history:{test_name}"] = ToolOutput(
tool_name="query_flake_history",
payload={
"failure_count": failures,
"pass_count": total_runs - failures,
"recent_failures": [
{"run_id": fake_short_sha(rng), "at": fake_timestamp(rng),
"note": "CI queue was full"}
for _ in range(min(3, failures))
],
},
cost_units=0.002,
)
difficulty = rng.choice(["easy", "medium", "hard"])
rationale = (
f"query_flake_history shows {failures}/{total_runs} intermittent failures. "
f"cluster_metrics:queue_depth is elevated, indicating CI is under load. "
f"Timeouts correlate with scheduler pressure — not a code bug. "
f"recent_commits shows no test-touching changes."
)
return Scenario(
schema_version="1.0",
scenario_id=scenario_id_for(self.family_name, seed),
family=self.family_name,
seed=seed,
ground_truth=GroundTruth(
label=self.label,
rationale=rationale,
is_ambiguous=False,
confidence_target=1.0,
),
failure_summary=summary,
tool_outputs=outputs,
informative_tools=self.informative_tools(),
minimal_evidence_set=self.minimal_evidence_set(),
correct_terminal_action=TerminalActionSpec(
primary="submit_diagnosis",
args={
"diagnosis": self.label.value,
"confidence": 0.9,
"secondary_actions": [{"name": "rerun_test"}],
},
acceptable_alternatives=[
{"primary": "submit_diagnosis",
"args": {"diagnosis": "timing_flake", "confidence": 0.85}},
],
),
metadata=ScenarioMetadata(
generator_version="1.0",
generated_at=fake_timestamp(rng),
source_log_hash=source_log_hash,
difficulty=difficulty,
),
)
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