Computer systems are often difficult to debug and understand. A common way of gaining insight into system behavior is to inspect execution logs and documentation. Unfortunately, manual inspection of logs is an arduous process and documentation is often incomplete and out of sync with the implementation.
This paper presents Synoptic, a tool that helps developers by inferring a concise and accurate system model. Unlike most related work, Synoptic does not require developer-written scenarios, specifications, negative execution examples, or other complex user input. Synoptic processes the logs most systems already produce and requires developers only to specify a set of regular expressions for parsing the logs.
Synoptic has two unique features. First, the model it produces satisfies three kinds of temporal invariants mined from the logs, improving accuracy over related approaches. Second, Synoptic uses refinement and coarsening to explore the space of models. This improves model efficiency and precision, compared to using just one approach.
In this paper, we formally prove that Synoptic always produces a model that satisfies exactly the temporal invariants mined from the log, and we argue that it does so efficiently. We empirically evaluate Synoptic through two user experience studies, one with a developer of a large, real-world system and another with 45 students in a distributed systems course. Developers used Synoptic-generated models to verify known bugs, diagnose new bugs, and increase their confidence in the correctness of their systems. None of the developers in our evaluation had a background in formal methods but were able to easily use Synoptic and detect implementation bugs in as little as a few minutes.
@inproceedings{Beschastnikh11fse,
author = {Ivan Beschastnikh and Yuriy Brun and Sigurd Schneider and Michael
Sloan and Michael D. Ernst},
title =
{Leveraging
existing instrumentation to automatically infer invariant-constrained
models},
booktitle = {Proceedings of the 8th Joint Meeting of the European Software
Engineering Conference and ACM SIGSOFT Symposium on the Foundations of
Software Engineering (ESEC/FSE)},
venue = {ESEC/FSE},
month = {September},
year = {2011},
date = {5--9},
pages = {267--277},
address = {Szeged, Hungary},
accept = {$\frac{34}{203} \approx 17\%$},
doi = {10.1145/2025113.2025151},
note = {DOI:
10.1145/2025113.2025151},
abstract = {Computer systems are often difficult to debug and understand. A
common way of gaining insight into system behavior is to inspect execution
logs and documentation. Unfortunately, manual inspection of logs is an
arduous process and documentation is often incomplete and out of sync with
the implementation.
This paper presents Synoptic, a tool that helps developers by inferring a
concise and accurate system model. Unlike most related work, Synoptic does
not require developer-written scenarios, specifications, negative execution
examples, or other complex user input. Synoptic processes the logs most
systems already produce and requires developers only to specify a set of
regular expressions for parsing the logs.
Synoptic has two unique features. First, the model it produces satisfies
three kinds of temporal invariants mined from the logs, improving accuracy
over related approaches. Second, Synoptic uses refinement and coarsening to
explore the space of models. This improves model efficiency and precision,
compared to using just one approach.
In this paper, we formally prove that Synoptic always produces a model that
satisfies exactly the temporal invariants mined from the log, and we argue
that it does so efficiently. We empirically evaluate Synoptic through two
user experience studies, one with a developer of a large, real-world system
and another with 45 students in a distributed systems course. Developers
used Synoptic-generated models to verify known bugs, diagnose new bugs, and
increase their confidence in the correctness of their systems. None of the
developers in our evaluation had a background in formal methods but were
able to easily use Synoptic and detect implementation bugs in as little as a
few minutes.},
fundedBy = {NSF CNS-0937060 to the CRA for the CIFellows Project, NSF CNS-0963754,
Saarbr{\"{u}}cken Graduate School of Computer Science},
}