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Failure sketching: a technique for automated root cause diagnosis of in-production failures

Authors:

Benjamin Schubert,

Cristiano Pereira,

George CandeaAuthors Info & Claims

SOSP '15: Proceedings of the 25th Symposium on Operating Systems Principles

Pages 344 - 360

https://doi.org/10.1145/2815400.2815412

Published: 04 October 2015 Publication History

Abstract

Developers spend a lot of time searching for the root causes of software failures. For this, they traditionally try to reproduce those failures, but unfortunately many failures are so hard to reproduce in a test environment that developers spend days or weeks as ad-hoc detectives. The shortcomings of many solutions proposed for this problem prevent their use in practice.

We propose failure sketching, an automated debugging technique that provides developers with an explanation ("failure sketch") of the root cause of a failure that occurred in production. A failure sketch only contains program statements that lead to the failure, and it clearly shows the differences between failing and successful runs; these differences guide developers to the root cause. Our approach combines static program analysis with a cooperative and adaptive form of dynamic program analysis.

We built Gist, a prototype for failure sketching that relies on hardware watchpoints and a new hardware feature for extracting control flow traces (Intel Processor Trace). We show that Gist can build failure sketches with low overhead for failures in systems like Apache, SQLite, and Memcached.

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Cited By

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Index Terms

Failure sketching: a technique for automated root cause diagnosis of in-production failures

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cover image ACM Conferences

SOSP '15: Proceedings of the 25th Symposium on Operating Systems Principles

October 2015

499 pages

ISBN:9781450338349

DOI:10.1145/2815400

General Chair:
Ethan Miller
UC Santa Cruz
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Published: 04 October 2015

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SOSP '15: ACM SIGOPS 25th Symposium on Operating Systems Principles

October 4 - 7, 2015

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SOSP '15 Paper Acceptance Rate 30 of 181 submissions, 17%;

Overall Acceptance Rate 174 of 961 submissions, 18%

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https://dl.acm.org/doi/10.1145/3698038.3698568
Qiu KRoychoudhury APaiva AAbreu RStorey M(2024)Autonomic Testing: Testing with Scenarios from ProductionProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3639802(156-158)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3639802
Wang DGalster MMorales-Trujillo M(2024)A systematic mapping study of bug reproduction and localizationInformation and Software Technology10.1016/j.infsof.2023.107338165:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107338
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