research-article

Unifying FSM-inference algorithms through declarative specification

Authors:

Ivan Beschastnikh,

Jenny Abrahamson,

Michael D. Ernst,

Arvind KrishnamurthyAuthors Info & Claims

ICSE '13: Proceedings of the 2013 International Conference on Software Engineering

Pages 252 - 261

Published: 18 May 2013 Publication History

Abstract

Logging system behavior is a staple development practice. Numerous powerful model inference algorithms have been proposed to aid developers in log analysis and system understanding. Unfortunately, existing algorithms are difficult to understand, extend, and compare. This paper presents InvariMint, an approach to specify model inference algorithms declaratively. We applied InvariMint to two model inference algorithms and present evaluation results to illustrate that InvariMint (1) leads to new fundamental insights and better understanding of existing algorithms, (2) simplifies creation of new algorithms, including hybrids that extend existing algorithms, and (3) makes it easy to compare and contrast previously published algorithms. Finally, algorithms specified with InvariMint can outperform their procedural versions.

References

[1]

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

Digital Library

[2]

D. Angluin. Finding Patterns Common to a Set of Strings. Journal of Computer and System Sciences, 21(1):46–62, 1980.

[3]

C. M. Antunes and A. L. Oliveira. Temporal Data Mining: An Overview. In KDD 2001 Workshop on Temporal Data Mining, 2001.

[4]

A. Bertolino, P. Inverardi, P. Pelliccione, and M. Tivoli. Automatic Synthesis of Behavior Protocols for Composable Web-Services. In ESEC/FSE, pages 141–150, 2009.

Digital Library

[5]

I. Beschastnikh, Y. Brun, J. Abrahamson, M. D. Ernst, and A. Krishnamurthy. Unifying FSM-Inference Algorithms through Declarative Specification. Technical Report UW-CSE-13-03-01, 2013.

[6]

I. Beschastnikh, Y. Brun, S. Schneider, and M. D. Ernst. Leveraging Existing Instrumentation to Automatically Infer Invariant-Constrained Models. In ESEC/FSE, pages 267–277, 2011.

Digital Library

[7]

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

Digital Library

[8]

B. Bollig, J.-P. Katoen, C. Kern, and M. Leucker. Learning Communicating Automata from MSCs. IEEE TSE, 36(3):390–408, 2010.

Digital Library

[9]

R. C. Carrasco and J. Oncina. Learning Stochastic Regular Grammars by Means of a State Merging Method. In ICGI, 1994.

Digital Library

[10]

E. Clarke, O. Grumberg, S. Jha, Y. Lu, and H. Veith. Counterexampleguided Abstraction Refinement. In Computer Aided Verification, pages 154–169, 2000.

Digital Library

[11]

J. E. Cook and A. L. Wolf. Discovering Models of Software Processes from Event-Based Data. ACM ToSEM, 7(3):215–249, 1998.

Digital Library

[12]

M. D. Ernst, J. Cockrell, W. G. Griswold, and D. Notkin. Dynamically Discovering Likely Program Invariants to Support Program Evolution. In ICSE, pages 213–224, 1999.

Digital Library

[13]

M. Gabel and Z. Su. Testing Mined Specifications. In FSE, 2012.

Digital Library

[14]

E. M. Gold. Language Identification in the Limit. Information and Control, 10(5):447–474, 1967.

[15]

S. Hangal and M. Lam. Tracking Down Software Bugs Using Automatic Anomaly Detection. In ICSE, 2002.

Digital Library

[16]

J. Hatcliff and M. Dwyer. Using the Bandera Tool Set to Model-Check Properties of Concurrent Java Software. In CONCUR, 2001.

Digital Library

[17]

J. E. Hopcroft. An n log n Algorithm for Minimizing States in a Finite Automaton. Technical report, Stanford Univ., 1971.

Digital Library

[18]

S. Kumar, S.-C. Khoo, A. Roychoudhury, and D. Lo. Inferring Class Level Specifications for Distributed Systems. In ICSE, 2012.

Digital Library

[19]

D. Lo and S.-C. Khoo. QUARK: Empirical Assessment of Automatonbased Specification Miners. In WCRE, 2006.

Digital Library

[20]

D. Lo and S.-C. Khoo. SMArTIC: Towards Building an Accurate, Robust and Scalable Specification Miner. In FSE, 2006.

Digital Library

[21]

D. Lo and S. Maoz. Scenario-Based and Value-Based Specification Mining: Better Together. In ASE, pages 387–396, 2010.

Digital Library

[22]

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

Digital Library

[23]

D. Lorenzoli, L. Mariani, and M. Pezzè. Automatic Generation of Software Behavioral Models. In ICSE, 2008.

Digital Library

[24]

L. Mariani and M. Pezzè. Dynamic Detection of COTS Component Incompatibility. IEEE Software, 24(5):76–85, 2007.

Digital Library

[25]

M. Pradel, P. Bichsel, and T. R. Gross. A Framework for the Evaluation of Specification Miners Based on Finite State Machines. In ICSM, 2010.

Digital Library

[26]

A. V. Raman and J. D. Patrick. The sk-strings Method for Inferring PFSA. In AIGILA, 1997.

[27]

S. P. Reiss and M. Renieris. Encoding Program Executions. In ICSE, 2001.

Digital Library

[28]

N. Walkinshaw and K. Bogdanov. Inferring Finite-State Models with Temporal Constraints. In ASE, pages 248–257, 2008.

Digital Library

[29]

W. Xu, L. Huang, A. Fox, D. Patterson, and M. Jordan. Experience Mining Google’s Production Console Logs. In SLAML, 2010.

Digital Library

[30]

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

Digital Library

[31]

T. Ziadi, M. da Silva, L. Hillah, and M. Ziane. A Fully Dynamic Approach to the Reverse Engineering of UML Sequence Diagrams. In ICECCS, pages 107–116, 2011.

Digital Library

Cited By

Nguyen HPhan HKhairunnesa SNguyen SYadavally AWang SRajan HNguyen T(2022)A Hybrid Approach for Inference between Behavioral Exception API Documentation and Implementations, and Its ApplicationsProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3560434(1-13)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3560434
Amar HBao LBusany NLo DMaoz SLeavens GGarcia APăsăreanu C(2018)Using finite-state models for log differencingProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236069(49-59)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3236069
Alsaeed ZYoung MAmann SNadi SNguyen HNguyen TKhatchadourian R(2018)Extending existing inference tools to mine dynamic APIsProceedings of the 2nd International Workshop on API Usage and Evolution10.1145/3194793.3194797(23-26)Online publication date: 2-Jun-2018
https://dl.acm.org/doi/10.1145/3194793.3194797
Show More Cited By

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Information

Published In

cover image ACM Conferences

ICSE '13: Proceedings of the 2013 International Conference on Software Engineering

May 2013

1561 pages

ISBN:9781467330763

General Chair:
David Notkin,
Program Chairs:
Betty H. C. Cheng,
Klaus Pohl

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

IEEE Press

Publication History

Published: 18 May 2013

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Research-article

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ICSE '13

Sponsor:

SIGSOFT

ICSE '13: 35th International Conference on Software Engineering

May 18 - 26, 2013

CA, San Francisco, USA

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Citations

Cited By

Nguyen HPhan HKhairunnesa SNguyen SYadavally AWang SRajan HNguyen T(2022)A Hybrid Approach for Inference between Behavioral Exception API Documentation and Implementations, and Its ApplicationsProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3560434(1-13)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3560434
Amar HBao LBusany NLo DMaoz SLeavens GGarcia APăsăreanu C(2018)Using finite-state models for log differencingProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236069(49-59)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3236069
Alsaeed ZYoung MAmann SNadi SNguyen HNguyen TKhatchadourian R(2018)Extending existing inference tools to mine dynamic APIsProceedings of the 2nd International Workshop on API Usage and Evolution10.1145/3194793.3194797(23-26)Online publication date: 2-Jun-2018
https://dl.acm.org/doi/10.1145/3194793.3194797
Alimadadi SMesbah APattabiraman KChaudron MCrnkovic IChechik MHarman M(2018)Inferring hierarchical motifs from execution tracesProceedings of the 40th International Conference on Software Engineering10.1145/3180155.3180216(776-787)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3180155.3180216
Yu XJin GChaudron MCrnkovic IChechik MHarman M(2018)Dataflow tunnelingProceedings of the 40th International Conference on Software Engineering10.1145/3180155.3180171(586-597)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3180155.3180171
Ermuth MPradel MZeller ARoychoudhury A(2016)Monkey see, monkey do: effective generation of GUI tests with inferred macro eventsProceedings of the 25th International Symposium on Software Testing and Analysis10.1145/2931037.2931053(82-93)Online publication date: 18-Jul-2016
https://dl.acm.org/doi/10.1145/2931037.2931053
Ohmann TStanley RBeschastnikh IBrun YDillon LVisser WWilliams L(2016)Visually reasoning about system and resource behaviorProceedings of the 38th International Conference on Software Engineering Companion10.1145/2889160.2889166(601-604)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2889160.2889166
Busany NMaoz SDillon LVisser WWilliams L(2016)Behavioral log analysis with statistical guaranteesProceedings of the 38th International Conference on Software Engineering10.1145/2884781.2884805(877-887)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2884781.2884805
Pastore FMariani LBertolino ACanfora GElbaum S(2015)ZoomInProceedings of the 37th International Conference on Software Engineering - Volume 110.5555/2818754.2818765(66-76)Online publication date: 16-May-2015
https://dl.acm.org/doi/10.5555/2818754.2818765
Busany NMaoz SDi Nitto EHarman MHeymans P(2015)Behavioral log analysis with statistical guaranteesProceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering10.1145/2786805.2803198(898-901)Online publication date: 30-Aug-2015
https://dl.acm.org/doi/10.1145/2786805.2803198
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