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

Get Access

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

Cited By

View all

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
https://dl.acm.org/doi/10.1145/3503222.3507701
Show More Cited By

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

Recommendations

Inferring models of concurrent systems from logs of their behavior with CSight
ICSE 2014: Proceedings of the 36th International Conference on Software Engineering

Concurrent systems are notoriously 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 ...
Robust log-based anomaly detection on unstable log data
ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Logs are widely used by large and complex software-intensive systems for troubleshooting. There have been a lot of studies on log-based anomaly detection. To detect the anomalies, the existing methods mainly construct a detection model using log event ...
Leveraging existing instrumentation to automatically infer invariant-constrained models
ESEC/FSE '11: Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering

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 ...

Comments

Information & Contributors

Information

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

ESEC/FSE'11

Sponsor:

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

Acceptance Rates

Overall Acceptance Rate 17 of 128 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

29
Total Citations
View Citations
260
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
https://dl.acm.org/doi/10.1145/3503222.3507701
Hossain MHan JSchneider JJiang JKabir MVersteeg S(2022)Extracting Formats of Service Messages with Varying PayloadsACM Transactions on Internet Technology10.1145/350315922:3(1-31)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1145/3503159
Farahmandpour ZSeyedmahmoudian MStojcevski A(2021)A Review on the Service Virtualisation and Its Structural PillarsApplied Sciences10.3390/app1105238111:5(2381)Online publication date: 8-Mar-2021
https://doi.org/10.3390/app11052381
Tang YLi TZhu RDu FWang JMa Z(2021)A Discovery Method for Hierarchical Software Execution Behavior Models Based on ComponentsScientific Programming10.1155/2021/47883572021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4788357
Schnepf NBadonnel RLahmadi AMerz S(2021)Automated Orchestration of Security Chains Driven by Process Learning*Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning10.1002/9781119675525.ch12(289-319)Online publication date: 3-Sep-2021
https://doi.org/10.1002/9781119675525.ch12
Johnson BBrun YMeliou ARothermel GBae D(2020)Causal testingProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380377(87-99)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380377
Taheri SBriggs IBurtscher MGopalakrishnan G(2019)DiffTrace: Efficient Whole-Program Trace Analysis and Diffing for Debugging2019 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER.2019.8891027(1-12)Online publication date: Sep-2019
https://doi.org/10.1109/CLUSTER.2019.8891027
Astekin MOzcan SSozer H(2019)Incremental Analysis of Large-Scale System Logs for Anomaly Detection2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006593(2119-2127)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006593
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

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

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations