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

Institute of Software Technology (7160)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Original language	English
Title of host publication	26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings
Editors	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
Publisher	Association of Computing Machinery
Pages	164-169
Number of pages	6
Volume	A
ISBN (Electronic)	9781450394437
DOIs	https://doi.org/10.1145/3546932.3546992
Publication status	Published - 12 Sept 2022
Event	26th ACM International Systems and Software Product Line Conference: ASPLC 2022 - Graz, Austria Duration: 12 Sept 2022 → 16 Sept 2022 http://2022.splc.net/

Conference

Conference	26th ACM International Systems and Software Product Line Conference
Abbreviated title	SPLC'22
Country/Territory	Austria
City	Graz
Period	12/09/22 → 16/09/22
Internet address	http://2022.splc.net/

Keywords

feature models
quality assurance
redundancy detection
testing and debugging
variability modeling

ASJC Scopus subject areas

Software

Access to Document

10.1145/3546932.3546992Licence: CC BY 4.0

Cite this

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 (Eds.), 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings (Vol. A, pp. 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. ed. / 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. Vol. A Association of Computing Machinery, 2022. p. 164-169.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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 (eds), 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings. vol. A, Association of Computing Machinery, pp. 164-169, 26th ACM International Systems and Software Product Line Conference, Graz, Austria, 12/09/22. https://doi.org/10.1145/3546932.3546992

Le VM , 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 VM, Horcas JM, editors, 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings. Vol. A. Association of Computing Machinery. 2022. p. 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. editor / 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. Vol. A Association of Computing Machinery, 2022. pp. 164-169

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ER -

WipeOutR: automated redundancy detection for feature models.

Abstract

Conference

Keywords

ASJC Scopus subject areas

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