debugging-strategies

Intermittent bugs require extensive logging of timing, state transitions, and external interactions. Look for race conditions in async code, check setTimeout and Promise ordering, and stress test by running the scenario many times under varied load.

Use git bisect to binary search your commit history. Mark the current commit as bad and a known working commit as good, then test each checked-out midpoint until git identifies the exact regressing commit.

Python's built-in cProfile module profiles function call times, and pstats sorts results by cumulative time to reveal bottlenecks. For interactive debugging, use breakpoint() or ipdb, and add structured logging to trace execution flow.

Monitor process.memoryUsage() for growing heap usage and generate heap snapshots with v8.writeHeapSnapshot(). In Chrome DevTools, compare heap snapshots before and after an action to find retained objects that should have been freed.

Production-only bugs usually stem from environment differences like dependency versions, data volume, configuration, or user permissions. Gather evidence from error tracking and logs, then reproduce locally with anonymized production data and matching environment settings.