**High-level approach**- Central Scheduler maintains DAG, worker pool, retry policies, and stable-failure aggregation.- Workers execute test tasks, report status and flaky evidence.- Retry manager applies intelligent backoff and quarantines truly flaky tests.- Aggregator consolidates final stable-failure report (tests failing consistently across retries and workers).**Components**- Scheduler: dependency resolution, task assignment, concurrency limits.- Worker: fetch task, run test, capture logs/metrics, send outcome.- Retry Manager: per-test state (attempts, last-fail-timestamp, flaky-score).- Aggregator: combine results, mark stable vs flaky.**Scheduler pseudocode**python
# simplified Python-like pseudocode
class Scheduler:
def __init__(self, dag, workers, retry_policy):
self.dag = dag # nodes: tests, edges: dependencies
self.ready = set(nodes with no unmet deps)
self.inflight = {}
self.results = {}
self.retry_policy = retry_policy
def schedule_loop(self):
while not done():
for worker in available_workers():
task = select_ready_test()
if task:
assign(worker, task)
self.inflight[task.id] = worker.id
handle_reports()
sleep(short_interval)
def handle_reports(self):
for report in collect_reports():
self.inflight.pop(report.test_id, None)
if report.status == "PASS":
mark_done(report.test_id)
else:
decision = RetryManager.decide(report.test_id, report)
if decision.retry:
enqueue_with_backoff(report.test_id, decision.delay)
else:
mark_failed(report.test_id, stable=decision.stable)
