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ai-test-orchestration AI-powered test orchestration skill covering intelligent test selection, risk-based test prioritization, flaky test management, test impact analysis, parallel execution optimization, and predictive test failure detection using machine learning.
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Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name AI Test Orchestration description AI-powered test orchestration skill covering intelligent test selection, risk-based test prioritization, flaky test management, test impact analysis, parallel execution optimization, and predictive test failure detection using machine learning. version 1.0.0 author thetestingacademy license MIT tags ["ai-testing","test-orchestration","test-prioritization","risk-based-testing","test-selection","parallel-testing","ml-testing"] testingTypes ["integration","e2e","unit"] frameworks ["playwright","jest","vitest","pytest"] languages ["typescript","javascript","python"] domains ["web","api","backend","devops"] agents ["claude-code","cursor","github-copilot","windsurf","codex","aider","continue","cline","zed","bolt","gemini-cli","amp"]
AI Test Orchestration Skill
You are an expert software engineer specializing in AI-powered test orchestration and intelligent test management. When the user asks you to implement, optimize, or debug test selection, prioritization, or parallel execution strategies, follow these detailed instructions.
Core Principles
Test the changed code first -- Prioritize tests that cover recently modified files and functions.Learn from history -- Use historical pass/fail data to predict which tests are likely to fail.Quarantine, don't ignore -- Flaky tests should be isolated and tracked, not deleted or skipped.Optimize for feedback speed -- Run the most likely-to-fail tests first so developers get fast signals.Distribute intelligently -- Split test suites across parallel workers based on historical duration, not file count.Measure and iterate -- Track metrics like time-to-first-failure, false positive rate, and test suite efficiency.Fail fast, verify thoroughly -- Fast feedback on PR checks, comprehensive verification on merge.
Project Structure
project/
src/
orchestrator/
test-selector.ts
risk-scorer.ts
impact-analyzer.ts
parallel-splitter.ts
flaky-detector.ts
prediction-model.ts
data/
test-history.ts
git-analysis.ts
coverage-map.ts
reporters/
orchestration-report.ts
metrics-collector.ts
config/
orchestration.config.ts
scripts/
collect-test-data.ts
train-model.py
analyze-flakiness.ts
tests/
orchestrator/
test-selector.test.ts
risk-scorer.test.ts
impact-analyzer.test.ts
Intelligent Test Selection Based on Code Changes
import { execSync } from 'child_process' ;
import { CoverageMap } from '../data/coverage-map' ;
interface TestSelection {
mustRun : string [];
shouldRun : string [];
canSkip
string
confidence
number
interface
ChangedFile
path
string
additions
number
deletions
number
changedFunctions
string
export
class
TestSelector
private
coverageMap
CoverageMap
constructor
coverageMap : CoverageMap
this
coverageMap
async
selectTests
baseBranch
string
'main'
Promise
TestSelection
const
this
getChangedFiles
const
this
coverageMap
getAllTests
const
new
Set
string
const
new
Set
string
for
const
of
const
this
coverageMap
getTestsCoveringFile
path
forEach
(t ) =>
add
if
changedFunctions
length
0
for
const
of
changedFunctions
const
this
coverageMap
getTestsCoveringFunction
path
forEach
(t ) =>
add
const
this
coverageMap
getDependentsOf
path
for
const
of
const
this
coverageMap
getTestsCoveringFile
forEach
(t ) =>
if
has
add
const
filter
(t ) =>
has
has
const
this
coverageMap
isComplete
0.95
0.7
return
mustRun
shouldRun
private
getChangedFiles
baseBranch
string
ChangedFile
const
execSync
`git diff --name-only --diff-filter=ACMR ${baseBranch} ...HEAD`
encoding
'utf-8'
trim
if
return
return
split
'\n'
map
(filePath ) =>
const
execSync
`git diff --numstat ${baseBranch} ...HEAD -- "${filePath} "`
encoding
'utf-8'
trim
const
split
'\t'
map
Number
const
execSync
`git diff -U0 ${baseBranch} ...HEAD -- "${filePath} "`
encoding
'utf-8'
const
this
extractChangedFunctions
return
path
private
extractChangedFunctions
diffContent
string
string
const
/^@@.*@@\s+(?:async\s+)?(?:function\s+)?(\w+)/gm
const
functions
string
let
match
RegExpExecArray
null
while
exec
null
push
1
return
new
Set
Risk-Based Test Prioritization
interface TestRiskScore {
testId : string ;
score : number ;
factors : RiskFactor [];
}
interface RiskFactor {
name : string ;
weight : number ;
value : number ;
contribution : number ;
}
interface TestHistory {
testId : string ;
recentResults : ('pass' | 'fail' | 'skip' )[];
averageDuration : number ;
lastFailedAt : Date | null ;
failureRate : number ;
flakinessScore : number ;
}
export class RiskScorer {
private weights = {
recentFailureRate : 30 ,
codeChangeProximity : 25 ,
historicalFlakiness : 15 ,
timeSinceLastRun : 10 ,
testAge : 5 ,
complexity : 10 ,
criticalPath : 5 ,
};
scoreTests (
tests : string [],
history : Map <string , TestHistory >,
changedFiles : string [],
coverageMap : Map <string , string []>
): TestRiskScore [] {
return tests
.map ((testId ) => this .scoreTest (testId, history, changedFiles, coverageMap))
.sort ((a, b ) => b.score - a.score );
}
private scoreTest (
testId : string ,
history : Map <string , TestHistory >,
changedFiles : string [],
coverageMap : Map <string , string []>
): TestRiskScore {
const testHistory = history.get (testId);
const factors : RiskFactor [] = [];
const failureRate = testHistory?.failureRate ?? 0 ;
factors.push ({
name : 'recentFailureRate' ,
weight : this .weights .recentFailureRate ,
value : failureRate,
contribution : failureRate * this .weights .recentFailureRate ,
});
const coveredFiles = coverageMap.get (testId) || [];
const overlapCount = coveredFiles.filter ((f ) => changedFiles.includes (f)).length ;
const proximity = coveredFiles.length > 0 ? overlapCount / coveredFiles.length : 0 ;
factors.push ({
name : 'codeChangeProximity' ,
weight : this .weights .codeChangeProximity ,
value : proximity,
contribution : proximity * this .weights .codeChangeProximity ,
});
const flakiness = testHistory?.flakinessScore ?? 0 ;
factors.push ({
name : 'historicalFlakiness' ,
weight : this .weights .historicalFlakiness ,
value : flakiness,
contribution : flakiness * this .weights .historicalFlakiness ,
});
const daysSinceRun = testHistory?.lastFailedAt
? (Date .now () - testHistory.lastFailedAt .getTime ()) / (1000 * 60 * 60 * 24 )
: 30 ;
const timeFactor = Math .min (daysSinceRun / 30 , 1 );
factors.push ({
name : 'timeSinceLastRun' ,
weight : this .weights .timeSinceLastRun ,
value : timeFactor,
contribution : timeFactor * this .weights .timeSinceLastRun ,
});
const score = factors.reduce ((sum, f ) => sum + f.contribution , 0 );
return { testId, score : Math .min (score, 100 ), factors };
}
}
Flaky Test Management
interface FlakyTestReport {
testId : string ;
flakinessScore : number ;
recentRuns : TestRun [];
pattern : FlakyPattern ;
recommendation : 'quarantine' | 'retry' | 'investigate' | 'stable' ;
}
type FlakyPattern =
| 'timing-dependent'
| 'order-dependent'
| 'resource-contention'
| 'network-dependent'
| 'random-data'
| 'unknown' ;
interface TestRun {
runId : string ;
result : 'pass' | 'fail' ;
duration : number ;
timestamp : Date ;
errorMessage ?: string ;
retryCount : number ;
}
export class FlakyDetector {
private readonly FLAKY_THRESHOLD = 0.1 ;
private readonly QUARANTINE_THRESHOLD = 0.3 ;
private readonly WINDOW_SIZE = 50 ;
analyze (testId : string , runs : TestRun []): FlakyTestReport {
const recentRuns = runs.slice (-this .WINDOW_SIZE );
const failCount = recentRuns.filter ((r ) => r.result === 'fail' ).length ;
const flakinessScore = failCount / recentRuns.length ;
const pattern = this .detectPattern (recentRuns);
let recommendation : FlakyTestReport ['recommendation' ];
if (flakinessScore >= this .QUARANTINE_THRESHOLD ) {
recommendation = 'quarantine' ;
} else if (flakinessScore >= this .FLAKY_THRESHOLD ) {
recommendation = 'investigate' ;
} else if (flakinessScore > 0 ) {
recommendation = 'retry' ;
} else {
recommendation = 'stable' ;
}
return { testId, flakinessScore, recentRuns, pattern, recommendation };
}
private detectPattern (runs : TestRun []): FlakyPattern {
const failures = runs.filter ((r ) => r.result === 'fail' );
if (failures.length === 0 ) return 'unknown' ;
const failDurations = failures.map ((r ) => r.duration );
const avgFailDuration = failDurations.reduce ((a, b ) => a + b, 0 ) / failDurations.length ;
const passDurations = runs.filter ((r ) => r.result === 'pass' ).map ((r ) => r.duration );
const avgPassDuration = passDurations.reduce ((a, b ) => a + b, 0 ) / passDurations.length ;
if (avgFailDuration > avgPassDuration * 3 ) {
return 'timing-dependent' ;
}
const networkErrors = failures.filter ((r ) =>
r.errorMessage ?.match (/ECONNREFUSED|ETIMEDOUT|fetch failed|network/i )
);
if (networkErrors.length > failures.length * 0.5 ) {
return 'network-dependent' ;
}
const failIndices = runs
.map ((r, i ) => (r.result === 'fail' ? i : -1 ))
.filter ((i ) => i >= 0 );
const clustered = failIndices.some (
(idx, i ) => i > 0 && idx - failIndices[i - 1 ] === 1
);
if (clustered) {
return 'order-dependent' ;
}
return 'unknown' ;
}
generateQuarantineConfig (reports : FlakyTestReport []): string {
const quarantined = reports
.filter ((r ) => r.recommendation === 'quarantine' )
.map ((r ) => r.testId );
return JSON .stringify (
{
quarantinedTests : quarantined,
retryConfig : {
maxRetries : 3 ,
retryDelay : 1000 ,
testsToRetry : reports
.filter ((r ) => r.recommendation === 'retry' )
.map ((r ) => r.testId ),
},
updatedAt : new Date ().toISOString (),
},
null ,
2
);
}
}
Retry Logic Integration
import { FlakyDetector , FlakyTestReport } from './flaky-detector' ;
interface RetryConfig {
maxRetries : number ;
backoffMs : number ;
backoffMultiplier : number ;
retryablePatterns : RegExp [];
}
const DEFAULT_RETRY_CONFIG : RetryConfig = {
maxRetries : 3 ,
backoffMs : 500 ,
backoffMultiplier : 2 ,
retryablePatterns : [
/ECONNREFUSED/ ,
/ETIMEDOUT/ ,
/net::ERR_CONNECTION_REFUSED/ ,
/Target closed/ ,
/Navigation timeout/ ,
/waiting for selector/i ,
],
};
export async function withRetry<T>(
testFn : () => Promise <T>,
config : RetryConfig = DEFAULT_RETRY_CONFIG
): Promise <{ result : T; attempts : number }> {
let lastError : Error | undefined ;
let delay = config.backoffMs ;
for (let attempt = 1 ; attempt <= config.maxRetries + 1 ; attempt++) {
try {
const result = await testFn ();
return { result, attempts : attempt };
} catch (error) {
lastError = error as Error ;
if (attempt > config.maxRetries ) break ;
const isRetryable = config.retryablePatterns .some ((pattern ) =>
pattern.test (lastError!.message )
);
if (!isRetryable) break ;
console .warn (
`Test attempt ${attempt} failed (retryable): ${lastError.message} . ` +
`Retrying in ${delay} ms...`
);
await new Promise ((resolve ) => setTimeout (resolve, delay));
delay *= config.backoffMultiplier ;
}
}
throw lastError;
}
import { defineConfig } from '@playwright/test' ;
export default defineConfig ({
retries : 2 ,
use : {
trace : 'on-first-retry' ,
video : 'on-first-retry' ,
},
projects : [
{
name : 'stable' ,
testMatch : /.*\.spec\.ts/ ,
retries : 0 ,
},
{
name : 'flaky-quarantine' ,
testMatch : /.*\.flaky\.spec\.ts/ ,
retries : 3 ,
use : {
trace : 'on' ,
},
},
],
});
Parallel Execution Optimization
interface TestBucket {
workerId : number ;
tests : string [];
estimatedDuration : number ;
}
interface TestMetadata {
testId : string ;
averageDuration : number ;
dependencies : string [];
resourceRequirements : string [];
}
export class ParallelSplitter {
splitByDuration (
tests : TestMetadata [],
workerCount : number
): TestBucket [] {
const sorted = [...tests].sort (
(a, b ) => b.averageDuration - a.averageDuration
);
const buckets : TestBucket [] = Array .from ({ length : workerCount }, (_, i ) => ({
workerId : i,
tests : [],
estimatedDuration : 0 ,
}));
for (const test of sorted) {
const minBucket = buckets.reduce ((min, bucket ) =>
bucket.estimatedDuration < min.estimatedDuration ? bucket : min
);
minBucket.tests .push (test.testId );
minBucket.estimatedDuration += test.averageDuration ;
}
return buckets;
}
splitWithConstraints (
tests : TestMetadata [],
workerCount : number
): TestBucket [] {
const resourceGroups = new Map <string , TestMetadata []>();
const noResourceTests : TestMetadata [] = [];
for (const test of tests) {
if (test.resourceRequirements .length === 0 ) {
noResourceTests.push (test);
} else {
const key = test.resourceRequirements .sort ().join (',' );
const group = resourceGroups.get (key) || [];
group.push (test);
resourceGroups.set (key, group);
}
}
const buckets = this .splitByDuration (noResourceTests, workerCount);
for (const [, group] of resourceGroups) {
const minBucket = buckets.reduce ((min, bucket ) =>
bucket.estimatedDuration < min.estimatedDuration ? bucket : min
);
for (const test of group) {
minBucket.tests .push (test.testId );
minBucket.estimatedDuration += test.averageDuration ;
}
}
return buckets;
}
}
CI Integration for Parallel Execution
name: Parallel Test Execution
on: [push ]
jobs:
plan:
name: Plan Test Distribution
runs-on: ubuntu-latest
outputs:
matrix: ${{ steps.split.outputs.matrix }}
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 20
- run: npm ci
- id: split
run: |
MATRIX=$(node scripts/plan-parallel.js --workers=4)
echo "matrix=$MATRIX" >> $GITHUB_OUTPUT
test:
name: Test Shard ${{ matrix.shard }}
needs: plan
runs-on: ubuntu-latest
strategy:
matrix: ${{ fromJson(needs.plan.outputs.matrix) }}
fail-fast: false
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 20
- run: npm ci
- run: |
npx vitest run --shard=${{ matrix.shard }}/${{ matrix.total }}
- uses: actions/upload-artifact@v4
if: always()
with:
name: test-results-${{ matrix.shard }}
path: test-results/
Predictive Test Failure Detection
"""
Train a simple model to predict which tests are likely to fail
based on code change features and historical test data.
"""
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score, recall_score, f1_score
import json
import pickle
def load_training_data (history_path: str ) -> pd.DataFrame:
"""Load and prepare training data from test history."""
with open (history_path) as f:
records = json.load(f)
rows = []
for record in records:
rows.append({
'test_id' : record['testId' ],
'files_changed' : record['filesChanged' ],
'lines_added' : record['linesAdded' ],
'lines_deleted' : record['linesDeleted' ],
'recent_failure_rate' : record['recentFailureRate' ],
'avg_duration' : record['avgDuration' ],
'days_since_last_change' : record['daysSinceLastChange' ],
'dependency_depth' : record['dependencyDepth' ],
'code_complexity' : record['codeComplexity' ],
'test_age_days' : record['testAgeDays' ],
'failed' : 1 if record['result' ] == 'fail' else 0 ,
})
return pd.DataFrame(rows)
def train_prediction_model (data: pd.DataFrame ):
"""Train a gradient boosting model for test failure prediction."""
features = [
'files_changed' , 'lines_added' , 'lines_deleted' ,
'recent_failure_rate' , 'avg_duration' , 'days_since_last_change' ,
'dependency_depth' , 'code_complexity' , 'test_age_days' ,
]
X = data[features]
y = data['failed' ]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2 , random_state=42 , stratify=y
)
model = GradientBoostingClassifier(
n_estimators=100 ,
max_depth=5 ,
learning_rate=0.1 ,
min_samples_split=10 ,
random_state=42 ,
)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print (f"Precision: {precision_score(y_test, y_pred):.3 f} " )
print (f"Recall: {recall_score(y_test, y_pred):.3 f} " )
print (f"F1 Score: {f1_score(y_test, y_pred):.3 f} " )
importance = dict (zip (features, model.feature_importances_))
print ("\nFeature Importance:" )
for feat, imp in sorted (importance.items(), key=lambda x: -x[1 ]):
print (f" {feat} : {imp:.3 f} " )
with open ('models/test-predictor.pkl' , 'wb' ) as f:
pickle.dump(model, f)
return model
if __name__ == '__main__' :
data = load_training_data('data/test-history.json' )
print (f"Training on {len (data)} records ({data['failed' ].sum ()} failures)" )
train_prediction_model(data)
Prediction Integration in TypeScript
import { execSync } from 'child_process' ;
interface PredictionResult {
testId : string ;
failureProbability : number ;
confidence : number ;
topFactors : { name : string ; contribution : number }[];
}
export class TestFailurePredictor {
private modelPath : string ;
constructor (modelPath : string = 'models/test-predictor.pkl' this .modelPath = modelPath;
}
predict (features : Map <string , Record <string , number >>): PredictionResult [] {
const input = JSON .stringify (Object .fromEntries (features));
const output = execSync (
`python3 scripts/predict.py --model=${this .modelPath} --input='${input} '` ,
{ encoding : 'utf-8' }
);
return JSON .parse (output);
}
predictHeuristic (
testId : string ,
recentFailureRate : number ,
filesChanged : number ,
linesChanged : number ,
daysSinceLastRun : number
): PredictionResult {
const failureProbability = Math .min (
1.0 ,
recentFailureRate * 0.4 +
Math .min (filesChanged / 20 , 1 ) * 0.25 +
Math .min (linesChanged / 500 , 1 ) * 0.2 +
Math .min (daysSinceLastRun / 30 , 1 ) * 0.15
);
return {
testId,
failureProbability,
confidence : 0.6 ,
topFactors : [
{ name : 'recentFailureRate' , contribution : recentFailureRate * 0.4 },
{ name : 'filesChanged' , contribution : Math .min (filesChanged / 20 , 1 ) * 0.25 },
{ name : 'linesChanged' , contribution : Math .min (linesChanged / 500 , 1 ) * 0.2 },
],
};
}
}
Test Impact Analysis from Git Diffs
import { execSync } from 'child_process' ;
interface ImpactAnalysis {
changedFiles : string [];
impactedModules : string [];
riskLevel : 'low' | 'medium' | 'high' | 'critical' ;
recommendedTestScope : 'unit' | 'integration' | 'e2e' | 'full' ;
}
export class GitAnalyzer {
analyzeImpact (baseBranch : string = 'main' ): ImpactAnalysis {
const diff = execSync (
`git diff --stat ${baseBranch} ...HEAD` ,
{ encoding : 'utf-8' }
);
const changedFiles = execSync (
`git diff --name-only ${baseBranch} ...HEAD` ,
{ encoding : 'utf-8' }
).trim ().split ('\n' ).filter (Boolean );
const impactedModules = this .identifyModules (changedFiles);
const riskLevel = this .assessRisk (changedFiles);
const recommendedTestScope = this .recommendScope (changedFiles, riskLevel);
return { changedFiles, impactedModules, riskLevel, recommendedTestScope };
}
private identifyModules (files : string []): string [] {
const modules = new Set <string >();
for (const file of files) {
const parts = file.split ('/' );
if (parts.length >= 2 ) {
modules.add (`${parts[0 ]} /${parts[1 ]} ` );
}
}
return [...modules];
}
private assessRisk (files : string []): ImpactAnalysis ['riskLevel' ] {
const criticalPatterns = [
/package\.json$/ ,
/\.env/ ,
/migration/ ,
/schema\./ ,
/auth\// ,
/middleware/ ,
];
const highRiskPatterns = [
/\.config\./ ,
/api\// ,
/database\// ,
];
if (files.some ((f ) => criticalPatterns.some ((p ) => p.test (f)))) {
return 'critical' ;
}
if (files.some ((f ) => highRiskPatterns.some ((p ) => p.test (f)))) {
return 'high' ;
}
if (files.length > 20 ) {
return 'high' ;
}
if (files.length > 5 ) {
return 'medium' ;
}
return 'low' ;
}
private recommendScope (
files : string [],
riskLevel : ImpactAnalysis ['riskLevel' ]
): ImpactAnalysis ['recommendedTestScope' ] {
if (riskLevel === 'critical' ) return 'full' ;
if (riskLevel === 'high' ) return 'e2e' ;
const hasApiChanges = files.some ((f ) => /api\// .test (f));
if (hasApiChanges) return 'integration' ;
return 'unit' ;
}
}
Metrics and Reporting
interface OrchestrationMetrics {
totalTests : number ;
testsSelected : number ;
testsSkipped : number ;
selectionAccuracy : number ;
timeToFirstFailure : number ;
totalDuration : number ;
estimatedTimeSaved : number ;
falsePositiveRate : number ;
parallelEfficiency : number ;
}
export class MetricsCollector {
private startTime : number = 0 ;
private firstFailureTime : number | null = null ;
start (): void {
this .startTime = Date .now ();
}
recordFailure (): void {
if (!this .firstFailureTime ) {
this .firstFailureTime = Date .now ();
}
}
generateReport (
totalTests : number ,
selectedTests : number ,
fullSuiteDuration : number
): OrchestrationMetrics {
const totalDuration = Date .now () - this .startTime ;
return {
totalTests,
testsSelected : selectedTests,
testsSkipped : totalTests - selectedTests,
selectionAccuracy : 0 ,
timeToFirstFailure : this .firstFailureTime
? this .firstFailureTime - this .startTime
: totalDuration,
totalDuration,
estimatedTimeSaved : fullSuiteDuration - totalDuration,
falsePositiveRate : 0 ,
parallelEfficiency : 0 ,
};
}
}
Best Practices
Collect coverage data on every CI run -- Store test-to-file mapping so the selector has accurate data.Retrain prediction models weekly -- Historical data changes as the codebase evolves; stale models lose accuracy.Set a maximum skip threshold -- Never skip more than 70% of tests on critical branches.Run the full suite nightly -- Even with smart selection, run everything at least once daily to catch drift.Track selection accuracy -- Measure how often skipped tests would have failed; aim for less than 1% miss rate.Use file-level coverage as a baseline -- Function-level analysis is better but file-level is a good starting point.Balance parallel shards by duration, not count -- 10 fast tests and 1 slow test are not balanced.Version your test metadata -- Store historical results in a database or JSON files committed to the repo.Alert on flakiness trends -- If flakiness increases, investigate before it erodes trust in the suite.Integrate with PR comments -- Report which tests were selected/skipped and why, directly on the pull request.
Anti-Patterns to Avoid
Skipping tests without coverage data -- Guessing which tests to skip leads to missed regressions.Retrying indefinitely -- Cap retries at 3; if a test fails 3 times, it is broken, not flaky.Ignoring flaky tests -- Unanswered flakiness erodes team confidence in the entire test suite.Static test sharding -- Splitting by file count instead of duration creates unbalanced workers.Overfitting the prediction model -- A model trained only on recent data misses rare edge cases.Running all tests on every commit -- This wastes CI resources and slows developer feedback loops.Not validating the selector -- Periodically run the full suite to verify the selector is not missing failures.Hardcoding test priorities -- Priority should be data-driven, not based on developer intuition.Sharing mutable state between parallel workers -- Tests sharing a database without isolation will break each other.No observability -- Without metrics on selection accuracy and time savings, you cannot improve the system.
Running the Orchestrator
npx tsx src/data/git-analysis.ts --base=main
npx tsx src/orchestrator/test-selector.ts --base=main --output=selected-tests.json
npx tsx src/orchestrator/risk-scorer.ts --input=selected-tests.json --output=prioritized.json
npx tsx scripts/analyze-flakiness.ts --history =data/test-history.json
npx tsx src/orchestrator/parallel-splitter.ts --workers=4 --input=prioritized.json
python3 scripts/train-model.py --data=data/test-history.json
npx vitest run $(cat selected-tests.json | jq -r '.mustRun[]' | tr '\n' ' ' )
npx tsx src/reporters/orchestration-report.ts --run-id=$(date +%s)
