Debug Determinism: The Sweet Spot for Replay-Based Debugging

Deterministic replay tools offer a compelling approach to debugging hard-to-reproduce bugs. Recent work on relaxed-deterministic replay techniques shows that replay debugging with low in-production overhead is possible. However, despite considerable progress, a replay-debugging system that offers not only low in-production runtime overhead but also high debugging utility, remains out of reach. To this end, we argue that the research community should strive for debug determinism —a new determinism model premised on the idea that effective debugging entails reproducing the same failure and the same root cause as the original execution. We present ideas on how to achieve and quantify debug determinism and give preliminary evidence that a debug deterministic system has potential to provide both low in-production overhead and high debugging utility

Low-Overhead Bug Fingerprinting for Fast Debugging

Abstract. There is a gap between the information available at the time of a software failure and the information actually shipped to developers in the corresponding bug report. As a result, identifying the cause of the bug based on this bug report is often difficult. To close this gap, we propose bug fingerprints—an augmentation of classic automated bug reports with runtime information about how the reported bug occurred in production. Classic automated bug reporting systems contain at most a coredump that describes the final manifestation of a bug. In contrast, bug fingerprints contain additional small amounts of highly relevant runtime information that helps understand how the bug occurred. We show how these “fingerprints ” can be used to speed up both manual and automated debugging. As a proof of concept, we present DCop, a system for collecting such runtime information about deadlocks and including it in the corresponding bug reports. The runtime overhead introduced by DCop is negligible (less than 0.17 % for the Apache Web server), so it is suitable for use in production.