WipeOutR: automated redundancy detection for feature models.

Viet-Man Le; Alexander Felfernig; Mathias Uta; Thi Ngoc Trang Tran; Cristian Vidal Silva

doi:10.1145/3546932.3546992

WipeOutR: automated redundancy detection for feature models.

Viet-Man Le, Alexander Felfernig, Mathias Uta, Thi Ngoc Trang Tran, Cristian Vidal Silva

Institut für Softwaretechnologie (7160)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two new algorithms that help to make feature model configuration as well as different kinds of analysis operations more efficient. Specifically, we focus on the automated identification of redundancies in feature models and cor-responding test suites. Redundant constraints in feature models can lead to low-performing configuration (solution) search and also to additional efforts in feature model debugging. Redundant feature model test cases can trigger inefficiencies in testing operations. In this paper, we introduce WipeOutR which is an algorithmic approach to support the automated identification of redundancies. This approach has the potential to significantly improve the quality of feature model development and configuration.

Originalsprache	englisch
Titel	26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings
Redakteure/-innen	Alexander Felfernig, Lidia Fuentes, Jane Cleland-Huang, Wesley K.G. Assuncao, Wesley K.G. Assuncao, Andreas Falkner, Maider Azanza, Miguel A. Rodriguez Luaces, Megha Bhushan, Laura Semini, Xavier Devroey, Claudia Maria Lima Werner, Christoph Seidl, Viet-Man Le, Jose Miguel Horcas
Herausgeber (Verlag)	Association of Computing Machinery
Seiten	164-169
Seitenumfang	6
Band	A
ISBN (elektronisch)	9781450394437
DOIs	https://doi.org/10.1145/3546932.3546992
Publikationsstatus	Veröffentlicht - 12 Sept. 2022
Veranstaltung	26th ACM International Systems and Software Product Line Conference: SPLC 2022 - Graz, Österreich Dauer: 12 Sept. 2022 → 16 Sept. 2022 http://2022.splc.net/

Konferenz

Konferenz	26th ACM International Systems and Software Product Line Conference
Kurztitel	SPLC'22
Land/Gebiet	Österreich
Ort	Graz
Zeitraum	12/09/22 → 16/09/22
Internetadresse	http://2022.splc.net/

ASJC Scopus subject areas

Software

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10.1145/3546932.3546992Lizenz: CC BY 4.0

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WipeOutR: automated redundancy detection for feature models
Viet Man Le (Redner/in)
14 Sept. 2022
Aktivität: Vortrag oder Präsentation › Vortrag bei Konferenz oder Fachtagung › Science to science

Dieses zitieren

Le, V-M., Felfernig, A., Uta, M., Tran, T. N. T., & Silva, C. V. (2022). WipeOutR: automated redundancy detection for feature models. in A. Felfernig, L. Fuentes, J. Cleland-Huang, W. K. G. Assuncao, W. K. G. Assuncao, A. Falkner, M. Azanza, M. A. R. Luaces, M. Bhushan, L. Semini, X. Devroey, C. M. L. Werner, C. Seidl, V-M. Le, & J. M. Horcas (Hrsg.), 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings (Band A, S. 164-169). Association of Computing Machinery. https://doi.org/10.1145/3546932.3546992

WipeOutR: automated redundancy detection for feature models. / Le, Viet-Man ; Felfernig, Alexander; Uta, Mathias et al.
26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings. Hrsg. / Alexander Felfernig; Lidia Fuentes; Jane Cleland-Huang; Wesley K.G. Assuncao; Wesley K.G. Assuncao; Andreas Falkner; Maider Azanza; Miguel A. Rodriguez Luaces; Megha Bhushan; Laura Semini; Xavier Devroey; Claudia Maria Lima Werner; Christoph Seidl; Viet-Man Le; Jose Miguel Horcas. Band A Association of Computing Machinery, 2022. S. 164-169.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Le, V-M , Felfernig, A, Uta, M, Tran, TNT & Silva, CV 2022, WipeOutR: automated redundancy detection for feature models. in A Felfernig, L Fuentes, J Cleland-Huang, WKG Assuncao, WKG Assuncao, A Falkner, M Azanza, MAR Luaces, M Bhushan, L Semini, X Devroey, CML Werner, C Seidl, V-M Le & JM Horcas (Hrsg.), 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings. Bd. A, Association of Computing Machinery, S. 164-169, 26th ACM International Systems and Software Product Line Conference, Graz, Österreich, 12/09/22. https://doi.org/10.1145/3546932.3546992

Le V-M , Felfernig A, Uta M, Tran TNT, Silva CV. WipeOutR: automated redundancy detection for feature models. in Felfernig A, Fuentes L, Cleland-Huang J, Assuncao WKG, Assuncao WKG, Falkner A, Azanza M, Luaces MAR, Bhushan M, Semini L, Devroey X, Werner CML, Seidl C, Le V-M, Horcas JM, Hrsg., 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings. Band A. Association of Computing Machinery. 2022. S. 164-169 doi: 10.1145/3546932.3546992

Le, Viet-Man ; Felfernig, Alexander ; Uta, Mathias et al. / WipeOutR: automated redundancy detection for feature models. 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings. Hrsg. / Alexander Felfernig ; Lidia Fuentes ; Jane Cleland-Huang ; Wesley K.G. Assuncao ; Wesley K.G. Assuncao ; Andreas Falkner ; Maider Azanza ; Miguel A. Rodriguez Luaces ; Megha Bhushan ; Laura Semini ; Xavier Devroey ; Claudia Maria Lima Werner ; Christoph Seidl ; Viet-Man Le ; Jose Miguel Horcas. Band A Association of Computing Machinery, 2022. S. 164-169

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title = "WipeOutR: automated redundancy detection for feature models.",

abstract = "Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two new algorithms that help to make feature model configuration as well as different kinds of analysis operations more efficient. Specifically, we focus on the automated identification of redundancies in feature models and cor-responding test suites. Redundant constraints in feature models can lead to low-performing configuration (solution) search and also to additional efforts in feature model debugging. Redundant feature model test cases can trigger inefficiencies in testing operations. In this paper, we introduce WipeOutR which is an algorithmic approach to support the automated identification of redundancies. This approach has the potential to significantly improve the quality of feature model development and configuration.",

keywords = "feature models, quality assurance, redundancy detection, testing and debugging, variability modeling",

author = "Viet-Man Le and Alexander Felfernig and Mathias Uta and Tran, {Thi Ngoc Trang} and Silva, {Cristian Vidal}",

year = "2022",

month = sep,

day = "12",

doi = "10.1145/3546932.3546992",

language = "English",

volume = "A",

pages = "164--169",

editor = "Alexander Felfernig and Lidia Fuentes and Jane Cleland-Huang and Assuncao, {Wesley K.G.} and Assuncao, {Wesley K.G.} and Andreas Falkner and Maider Azanza and Luaces, {Miguel A. Rodriguez} and Megha Bhushan and Laura Semini and Xavier Devroey and Werner, {Claudia Maria Lima} and Christoph Seidl and Viet-Man Le and Horcas, {Jose Miguel}",

booktitle = "26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings",

publisher = "Association of Computing Machinery",

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note = "26th ACM International Systems and Software Product Line Conference : ASPLC 2022, SPLC'22 ; Conference date: 12-09-2022 Through 16-09-2022",

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AU - Silva, Cristian Vidal

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Y1 - 2022/9/12

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AB - Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two new algorithms that help to make feature model configuration as well as different kinds of analysis operations more efficient. Specifically, we focus on the automated identification of redundancies in feature models and cor-responding test suites. Redundant constraints in feature models can lead to low-performing configuration (solution) search and also to additional efforts in feature model debugging. Redundant feature model test cases can trigger inefficiencies in testing operations. In this paper, we introduce WipeOutR which is an algorithmic approach to support the automated identification of redundancies. This approach has the potential to significantly improve the quality of feature model development and configuration.

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KW - quality assurance

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BT - 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings

A2 - Felfernig, Alexander

A2 - Fuentes, Lidia

A2 - Cleland-Huang, Jane

A2 - Assuncao, Wesley K.G.

A2 - Falkner, Andreas

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A2 - Luaces, Miguel A. Rodriguez

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ER -

WipeOutR: automated redundancy detection for feature models.

Abstract

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ASJC Scopus subject areas

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WipeOutR: automated redundancy detection for feature models

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