Mining Branching-Time Scenarios

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in...

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Main Authors: FAHLAND, Dirk, LO, David, MAOZ, Shahar
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/2029
https://ink.library.smu.edu.sg/context/sis_research/article/3028/viewcontent/FahlandLM_2013_ase_branching_mining.pdf
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spelling sg-smu-ink.sis_research-30282015-12-12T09:09:31Z Mining Branching-Time Scenarios FAHLAND, Dirk LO, David MAOZ, Shahar Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows the unique contribution of mining branching-time scenarios to the state-of-the-art in specification mining. 2013-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2029 info:doi/10.1109/ASE.2013.6693102 https://ink.library.smu.edu.sg/context/sis_research/article/3028/viewcontent/FahlandLM_2013_ase_branching_mining.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University data mining formal verification program testing statistical analysis Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic data mining
formal verification
program testing
statistical analysis
Software Engineering
spellingShingle data mining
formal verification
program testing
statistical analysis
Software Engineering
FAHLAND, Dirk
LO, David
MAOZ, Shahar
Mining Branching-Time Scenarios
description Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows the unique contribution of mining branching-time scenarios to the state-of-the-art in specification mining.
format text
author FAHLAND, Dirk
LO, David
MAOZ, Shahar
author_facet FAHLAND, Dirk
LO, David
MAOZ, Shahar
author_sort FAHLAND, Dirk
title Mining Branching-Time Scenarios
title_short Mining Branching-Time Scenarios
title_full Mining Branching-Time Scenarios
title_fullStr Mining Branching-Time Scenarios
title_full_unstemmed Mining Branching-Time Scenarios
title_sort mining branching-time scenarios
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/2029
https://ink.library.smu.edu.sg/context/sis_research/article/3028/viewcontent/FahlandLM_2013_ase_branching_mining.pdf
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