demonstration

Synoptic: studying logged behavior with inferred models

Authors:

Ivan Beschastnikh,

Jenny Abrahamson,

Yuriy Brun,

Michael D. ErnstAuthors Info & Claims

ESEC/FSE '11: Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering

Pages 448 - 451

https://doi.org/10.1145/2025113.2025188

Published: 09 September 2011 Publication History

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Abstract

Logging is a powerful method for capturing program activity and state during an execution. However, log inspection remains a tedious activity, with developers often piecing together what went on from multiple log lines and across many files. This paper describes Synoptic, a tool that takes logs as input and outputs a finite state machine that models the process generating the logs. The paper overviews the model inference algorithms. Then, it describes the Synoptic tool, which is designed to support a rich log exploration workflow.

References

[1]

Pattern (Java Platform SE 7 b141) http://download.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html. Accessed July 1, 2011.

Google Scholar

[2]

M. Acharya, T. Xie, J. Pei, and J. Xu. Mining API Patterns as Partial Orders from Source Code: From Usage Scenarios to Specifications. In Proc. of FSE, 2007.

Digital Library

Google Scholar

[3]

I. Beschastnikh, Y. Brun, S. Schneider, M. Sloan, and M. D. Ernst. Leveraging Existing Instrumentation to Automatically Infer Invariant-Constrained Models. In Proc. of FSE, 2011.

Digital Library

Google Scholar

[4]

A. W. Biermann and J. A. Feldman. On the Synthesis of Finite-State Machines from Samples of Their Behavior. IEEE Trans. Comput., 21(6):592--597, 1972.

Digital Library

Google Scholar

[5]

E. Clarke, O. Grumberg, S. Jha, Y. Lu, and H. Veith. Counterexample-guided Abstraction Refinement. In Proc. of CAV, 2000.

Digital Library

Google Scholar

[6]

J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. Commun. ACM, 51(1):107--113, 2008.

Digital Library

Google Scholar

[7]

M. B. Dwyer, G. S. Avrunin, and J. C. Corbett. Patterns in Property Specifications for Finite-State Verification. In Proc. of ICSE, 1999.

Digital Library

Google Scholar

[8]

D. Lo, L. e. Mariani, and M. Pezzè. Automatic Steering of Behavioral Model Inference. In Proc. of FSE, 2009.

Digital Library

Google Scholar

[9]

S. Schneider, I. Beschastnikh, S. Chernyak, M. D. Ernst, and Y. Brun. Synoptic: Summarizing System Logs with Refinement. In Proc. of SLAML, 2010.

Digital Library

Google Scholar

[10]

J. Yang, D. Evans, D. Bhardwaj, T. Bhat, and M. Das. Perracotta: Mining Temporal API Rules from Imperfect Traces. In Proc. of ICSE, 2006.

Digital Library

Google Scholar

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Brun YLin TSomerville JMyers EEbner N(2023)Blindspots in Python and Java APIs Result in Vulnerable CodeACM Transactions on Software Engineering and Methodology10.1145/357185032:3(1-31)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1145/3571850
Yang YWang LGu JLi Y(2023)Capturing Request Execution Path for Understanding Service Behavior and Detecting Anomalies Without Code InstrumentationIEEE Transactions on Services Computing10.1109/TSC.2022.314994916:2(996-1010)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSC.2022.3149949
Ma JZuo GLoughlin KZhang HQuinn AKasikci BFalsafi BFerdman MLu SWenisch T(2022)Debugging in the brave new world of reconfigurable hardwareProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507701(946-962)Online publication date: 28-Feb-2022
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Index Terms

Synoptic: studying logged behavior with inferred models
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

ESEC/FSE '11: Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering

September 2011

548 pages

ISBN:9781450304436

DOI:10.1145/2025113

General Chair:
Tibor Gyimóthy
University of Szeged, Hungary
,
Program Chair:
Andreas Zeller
Saarland University, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 September 2011

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SIGSOFT

ESEC/FSE'11: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

September 5 - 9, 2011

Szeged, Hungary

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https://dl.acm.org/doi/10.1145/3571850
Yang YWang LGu JLi Y(2023)Capturing Request Execution Path for Understanding Service Behavior and Detecting Anomalies Without Code InstrumentationIEEE Transactions on Services Computing10.1109/TSC.2022.314994916:2(996-1010)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSC.2022.3149949
Ma JZuo GLoughlin KZhang HQuinn AKasikci BFalsafi BFerdman MLu SWenisch T(2022)Debugging in the brave new world of reconfigurable hardwareProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507701(946-962)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507701
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Abstract

References

Cited By

Index Terms

Recommendations

Inferring models of concurrent systems from logs of their behavior with CSight

Robust log-based anomaly detection on unstable log data

Leveraging existing instrumentation to automatically infer invariant-constrained models

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