Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems

Sebastian Lubos; Alexander Felfernig; Viet-Man Le; Thi Ngoc Trang Tran; David Benavides; José A. Zamudio; Damian Garber

doi:10.1145/3579027.3608982

Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems

Sebastian Lubos, Alexander Felfernig, Viet-Man Le^*, Thi Ngoc Trang Tran, David Benavides, José A. Zamudio, Damian Garber

^*Corresponding author for this work

Institute of Software Technology (7160)

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

Abstract

The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

Original language	English
Title of host publication	27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings
Editors	Paolo Arcaini, Maurice H. ter Beek, Gilles Perrouin, Iris Reinhartz-Berger, Miguel R. Luaces, Christa Schwanninger, Shaukat Ali, Mahsa Varshosaz, Angelo Gargantini, Stefania Gnesi, Malte Lochau, Laura Semini, Hironori Washizaki
Place of Publication	New York
Publisher	Association of Computing Machinery
Pages	111–116
Number of pages	6
Volume	A
ISBN (Electronic)	979-8-4007-0091-0
DOIs	https://doi.org/10.1145/3579027.3608982
Publication status	Published - 28 Aug 2023
Event	27th ACM International Systems and Software Product Line Conference: SPLC 2023 - National Institute of Informatics, Tokyo, Japan Duration: 28 Aug 2023 → 1 Sept 2023 https://2023.splc.net/ https://2023.splc.net

Publication series

Name	ACM International Conference Proceeding Series
Volume	A-1

Conference

Conference	27th ACM International Systems and Software Product Line Conference
Abbreviated title	SPLC '23
Country/Territory	Japan
City	Tokyo
Period	28/08/23 → 1/09/23
Internet address	https://2023.splc.net/ https://2023.splc.net

Keywords

analysis operations
constraint solving
constraint-based recommender systems
feature models
Variability models

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3579027.3608982Licence: CC BY 4.0

3579027.3608982Final published version, 783 KBLicence: CC BY 4.0

Cite this

Lubos, S., Felfernig, A., Le, V.-M., Tran, T. N. T., Benavides, D., Zamudio, J. A., & Garber, D. (2023). Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems. In P. Arcaini, M. H. ter Beek, G. Perrouin, I. Reinhartz-Berger, M. R. Luaces, C. Schwanninger, S. Ali, M. Varshosaz, A. Gargantini, S. Gnesi, M. Lochau, L. Semini, & H. Washizaki (Eds.), 27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings (Vol. A, pp. 111–116). (ACM International Conference Proceeding Series; Vol. A-1). Association of Computing Machinery. https://doi.org/10.1145/3579027.3608982

Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems. / Lubos, Sebastian ; Felfernig, Alexander ; Le, Viet-Man et al.
27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings. ed. / Paolo Arcaini; Maurice H. ter Beek; Gilles Perrouin; Iris Reinhartz-Berger; Miguel R. Luaces; Christa Schwanninger; Shaukat Ali; Mahsa Varshosaz; Angelo Gargantini; Stefania Gnesi; Malte Lochau; Laura Semini; Hironori Washizaki. Vol. A New York: Association of Computing Machinery, 2023. p. 111–116 (ACM International Conference Proceeding Series; Vol. A-1).

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

Lubos, S , Felfernig, A , Le, V-M , Tran, TNT, Benavides, D, Zamudio, JA & Garber, D 2023, Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems. in P Arcaini, MH ter Beek, G Perrouin, I Reinhartz-Berger, MR Luaces, C Schwanninger, S Ali, M Varshosaz, A Gargantini, S Gnesi, M Lochau, L Semini & H Washizaki (eds), 27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings. vol. A, ACM International Conference Proceeding Series, vol. A-1, Association of Computing Machinery, New York, pp. 111–116, 27th ACM International Systems and Software Product Line Conference, Tokyo, Japan, 28/08/23. https://doi.org/10.1145/3579027.3608982

Lubos S , Felfernig A , Le VM , Tran TNT, Benavides D, Zamudio JA et al. Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems. In Arcaini P, ter Beek MH, Perrouin G, Reinhartz-Berger I, Luaces MR, Schwanninger C, Ali S, Varshosaz M, Gargantini A, Gnesi S, Lochau M, Semini L, Washizaki H, editors, 27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings. Vol. A. New York: Association of Computing Machinery. 2023. p. 111–116. (ACM International Conference Proceeding Series). doi: 10.1145/3579027.3608982

Lubos, Sebastian ; Felfernig, Alexander ; Le, Viet-Man et al. / Analysis Operations On The Run : Feature Model Analysis in Constraint-based Recommender Systems. 27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings. editor / Paolo Arcaini ; Maurice H. ter Beek ; Gilles Perrouin ; Iris Reinhartz-Berger ; Miguel R. Luaces ; Christa Schwanninger ; Shaukat Ali ; Mahsa Varshosaz ; Angelo Gargantini ; Stefania Gnesi ; Malte Lochau ; Laura Semini ; Hironori Washizaki. Vol. A New York : Association of Computing Machinery, 2023. pp. 111–116 (ACM International Conference Proceeding Series).

@inproceedings{4b73102636b1442bba60d2c4a8ef7898,

title = "Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems",

abstract = "The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.",

keywords = "analysis operations, constraint solving, constraint-based recommender systems, feature models, Variability models",

author = "Sebastian Lubos and Alexander Felfernig and Viet-Man Le and Tran, {Thi Ngoc Trang} and David Benavides and Zamudio, {Jos{\'e} A.} and Damian Garber",

note = "Funding Information: The work presented in this paper has been partially conducted within the scope of the OpenSpace project funded by the Austrian research promotion agency (FO999891127) and the Data-pl project supported by FEDER/Ministry of Science and Innovation/State Research Agency (PID2022-138486OB-I00). Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 27th ACM International Systems and Software Product Line Conference : SPLC 2023, SPLC '23 ; Conference date: 28-08-2023 Through 01-09-2023",

year = "2023",

month = aug,

day = "28",

doi = "10.1145/3579027.3608982",

language = "English",

volume = "A",

series = "ACM International Conference Proceeding Series",

publisher = "Association of Computing Machinery",

pages = "111–116",

editor = "Paolo Arcaini and {ter Beek}, {Maurice H.} and Gilles Perrouin and Iris Reinhartz-Berger and Luaces, {Miguel R.} and Christa Schwanninger and Shaukat Ali and Mahsa Varshosaz and Angelo Gargantini and Stefania Gnesi and Malte Lochau and Laura Semini and Hironori Washizaki",

booktitle = "27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings",

address = "United States",

url = "https://2023.splc.net/, https://2023.splc.net",

}

TY - GEN

T1 - Analysis Operations On The Run

T2 - 27th ACM International Systems and Software Product Line Conference

AU - Lubos, Sebastian

AU - Felfernig, Alexander

AU - Le, Viet-Man

AU - Tran, Thi Ngoc Trang

AU - Benavides, David

AU - Zamudio, José A.

AU - Garber, Damian

N1 - Funding Information: The work presented in this paper has been partially conducted within the scope of the OpenSpace project funded by the Austrian research promotion agency (FO999891127) and the Data-pl project supported by FEDER/Ministry of Science and Innovation/State Research Agency (PID2022-138486OB-I00). Publisher Copyright: © 2023 Owner/Author.

PY - 2023/8/28

Y1 - 2023/8/28

N2 - The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

AB - The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

KW - analysis operations

KW - constraint solving

KW - constraint-based recommender systems

KW - feature models

KW - Variability models

UR - http://www.scopus.com/inward/record.url?scp=85175957558&partnerID=8YFLogxK

U2 - 10.1145/3579027.3608982

DO - 10.1145/3579027.3608982

M3 - Conference paper

VL - A

T3 - ACM International Conference Proceeding Series

SP - 111

EP - 116

BT - 27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Proceedings

A2 - Arcaini, Paolo

A2 - ter Beek, Maurice H.

A2 - Perrouin, Gilles

A2 - Reinhartz-Berger, Iris

A2 - Luaces, Miguel R.

A2 - Schwanninger, Christa

A2 - Ali, Shaukat

A2 - Varshosaz, Mahsa

A2 - Gargantini, Angelo

A2 - Gnesi, Stefania

A2 - Lochau, Malte

A2 - Semini, Laura

A2 - Washizaki, Hironori

PB - Association of Computing Machinery

CY - New York

Y2 - 28 August 2023 through 1 September 2023

ER -

Analysis Operations On The Run: Feature Model Analysis in Constraint-based Recommender Systems

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint