Standard

flexfringe : A Passive Automaton Learning Package. / Verwer, Sicco; Hammerschmidt, Christian A.

2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. ed. / L. O'Conner. Piscataway : IEEE, 2017. p. 638-642.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Verwer, S & Hammerschmidt, CA 2017, flexfringe: A Passive Automaton Learning Package. in L O'Conner (ed.), 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. IEEE, Piscataway, pp. 638-642, ICSME 2017, Shanghai, China, 17/09/17. https://doi.org/10.1109/ICSME.2017.58

APA

Verwer, S., & Hammerschmidt, C. A. (2017). flexfringe: A Passive Automaton Learning Package. In L. O'Conner (Ed.), 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017 (pp. 638-642). Piscataway: IEEE. https://doi.org/10.1109/ICSME.2017.58

Vancouver

Verwer S, Hammerschmidt CA. flexfringe: A Passive Automaton Learning Package. In O'Conner L, editor, 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. Piscataway: IEEE. 2017. p. 638-642 https://doi.org/10.1109/ICSME.2017.58

Author

Verwer, Sicco ; Hammerschmidt, Christian A. / flexfringe : A Passive Automaton Learning Package. 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. editor / L. O'Conner. Piscataway : IEEE, 2017. pp. 638-642

BibTeX

@inproceedings{9cdeb004de4741b399704ec9b3a980bf,
title = "flexfringe: A Passive Automaton Learning Package",
abstract = "Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineering these models from execution traces and log files, in turn, can accelerate and improve the software development and inform domain experts about the processes actually executed in a system. We present name, an open-source software tool to learn variants of finite state automata from traces using a state-of-the-art evidence-driven state-merging algorithm at its core. We embrace the need for customized models and tailored learning heuristics in different application domains by providing a flexible, extensible interface.",
keywords = "Learning Automata, Tools, Software algorithms, Software, Heuristic algorithms, Machine learning algorithms, Algorithm design and analysis",
author = "Sicco Verwer and Hammerschmidt, {Christian A.}",
year = "2017",
doi = "10.1109/ICSME.2017.58",
language = "English",
isbn = "978-1-5386-0993-4",
pages = "638--642",
editor = "L. O'Conner",
booktitle = "2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - flexfringe

T2 - A Passive Automaton Learning Package

AU - Verwer, Sicco

AU - Hammerschmidt, Christian A.

PY - 2017

Y1 - 2017

N2 - Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineering these models from execution traces and log files, in turn, can accelerate and improve the software development and inform domain experts about the processes actually executed in a system. We present name, an open-source software tool to learn variants of finite state automata from traces using a state-of-the-art evidence-driven state-merging algorithm at its core. We embrace the need for customized models and tailored learning heuristics in different application domains by providing a flexible, extensible interface.

AB - Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineering these models from execution traces and log files, in turn, can accelerate and improve the software development and inform domain experts about the processes actually executed in a system. We present name, an open-source software tool to learn variants of finite state automata from traces using a state-of-the-art evidence-driven state-merging algorithm at its core. We embrace the need for customized models and tailored learning heuristics in different application domains by providing a flexible, extensible interface.

KW - Learning Automata

KW - Tools

KW - Software algorithms

KW - Software

KW - Heuristic algorithms

KW - Machine learning algorithms

KW - Algorithm design and analysis

U2 - 10.1109/ICSME.2017.58

DO - 10.1109/ICSME.2017.58

M3 - Conference contribution

SN - 978-1-5386-0993-4

SP - 638

EP - 642

BT - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017

A2 - O'Conner, L.

PB - IEEE

CY - Piscataway

ER -

ID: 33113662