short-paper

FM fact label: a configurable and interactive visualization of feature model characterizations

Authors:

Jose M. Horcas,

Jose A. Galindo,

David BenavidesAuthors Info & Claims

SPLC '22: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B

Pages 42 - 45

https://doi.org/10.1145/3503229.3547025

Published: 12 September 2022 Publication History

Abstract

Recognizing specific characteristics of feature models (FM) can be challenging due to the different nature and domains of the models. There are several metrics to characterize FMs. However, there is no standard way to visualize and identify the properties that make an FM unique and distinguishable. We propose FM Fact Label as a tool to visualize an FM characterization based on its metadata, structural measures, and analytical metrics. Although existing tools can provide a visualization of the FM and report some metrics, the feature diagram of large-scale FMs becomes ineffective to take an overall shape of the FM properties. Moreover, the reported metrics are often embedded in the tool user interface, preventing further analysis. FM Fact Label is a standalone web-based tool that provides a configurable and interactive visualization of FM characterizations that can be exported to several formats. Our contribution becomes important because the Universal Variability Language (UVL) is starting to gain attraction in the software product line community as a unified textual language to specify FMs and share knowledge. With this contribution, we help to advance the UVL ecosystem one step forward while providing a common representation for the results of existing analysis tools.

References

[1]

Dur Abuzaid and Scott Titang. 2014. The Visualization of Software Quality Metrics-A SLR. Bachelor thesis. Univ. of Gothenburg. Chalmers Univ. of Technology.

[2]

Ebrahim Bagheri and Dragan Gasevic. 2011. Assessing the maintainability of software product line feature models using structural metrics. Softw. Qual. J. 19, 3 (2011), 579--612.

Digital Library

[3]

David Benavides, Rick Rabiser, Don S. Batory, and Mathieu Acher. 2019. First international workshop on languages for modelling variability (MODEVAR). In 23rd SPLC, Vol. A. 46:1.

Digital Library

[4]

Carla Ilane Moreira Bezerra, Rossana M. C. Andrade, and José Maria Monteiro. 2015. Measures for Quality Evaluation of Feature Models. In 14th ICSR, Vol. 8919. 282--297.

[5]

Carla I. M. Bezerra, Rossana M. C. Andrade, and José Maria Monteiro. 2017. Exploring quality measures for the evaluation of feature models: a case study. J. Syst. Softw. 131 (2017), 366--385.

[6]

Carla I. M. Bezerra, Rafael Lima, and Publio Silva. 2021. DyMMer 2.0: A Tool for Dynamic Modeling and Evaluation of Feature Model. In 35th Brazilian Symposium on Software Engineering (SBES). 121--126.

Digital Library

[7]

Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D³ Data-Driven Documents. IEEE Trans. Vis. Comput. Graph. 17, 12 (2011), 2301--2309.

Digital Library

[8]

Ferruccio Damiani, Michael Lienhardt, and Luca Paolini. 2022. On logical and extensional characterizations of attributed feature models. Theor. Comput. Sci. 912 (2022), 56--80.

Digital Library

[9]

Kevin Feichtinger, Johann Stöbich, Dario Romano, and Rick Rabiser. 2021. TRAVART: An Approach for Transforming Variability Models. In VaMoS. 8:1--8:10.

Digital Library

[10]

Food and Drug Administration, HHS. 2016. Food labeling: revision of the nutrition and supplement facts labels. Federal register 81, 103 (2016), 33741--33999.

[11]

José A. Galindo and David Benavides. 2020. A Python framework for the automated analysis of feature models: A first step to integrate community efforts. In 24th SPLC, Vol. B. 52--55.

Digital Library

[12]

Ruben Heradio, David Fernández-Amorós, Christoph Mayr-Dorn, and Alexander Egyed. 2019. Supporting the statistical analysis of variability models. In 41st ICSE. 843--853.

Digital Library

[13]

Alexander Knüppel, Thomas Thüm, Stephan Mennicke, Jens Meinicke, and Ina Schaefer. 2018. Is There a Mismatch between Real-World Feature Models and Product-Line Research?. In ESEC/FSE. 53--54. https://dl.gi.de/20.500.12116/16312

[14]

Jia Hui Liang, Vijay Ganesh, Krzysztof Czarnecki, and Venkatesh Raman. 2015. SAT-Based Analysis of Large Real-World Feature Models is Easy. In 19th SPLC. 91--100.

Digital Library

[15]

Roberto Erick Lopez-Herrejon, Sheny Illescas, and Alexander Egyed. 2018. A systematic mapping study of information visualization for software product line engineering. J. Softw. Evol. Process. 30, 2 (2018).

[16]

Marcilio Mendonca, Moises Branco, and Donald Cowan. 2009. S.P.L.O.T.: Software Product Lines Online Tools. In OOPSLA.

Digital Library

[17]

Stephen R. Midway. 2020. Principles of Effective Data Visualization. Patterns 1, 9 (2020), 100141.

[18]

David Romero, José A. Galindo, José Miguel Horcas, and David Benavides. 2021. A first prototype of a new repository for feature model exchange and knowledge sharing. In 25th SPLC, Vol. B. 80--85.

Digital Library

[19]

Anna Schmitt, Christian Bettinger, and Georg Rock. 2018. Glencoe - A Tool for Specification, Visualization and Formal Analysis of Product Lines. In 25th Trans. Eng. 665--673.

[20]

Chico Sundermann, Kevin Feichtinger, Dominik Engelhardt, Rick Rabiser, and Thomas Thüm. 2021. Yet another textual variability language?: a community effort towards a unified language. In 25th SPLC, Vol. A. 136--147.

Digital Library

[21]

Chico Sundermann, Tobias Heß, Dominik Engelhardt, Rahel Arens, Johannes Herschel, Kevin Jedelhauser, Benedikt Jutz, Sebastian Krieter, and Ina Schaefer. 2021. Integration of UVL in FeatureIDE. In 25th SPLC, Vol. B. 73--79.

Digital Library

[22]

Maurice H. ter Beek, Klaus Schmid, and Holger Eichelberger. 2019. Textual variability modeling languages: an overview and considerations. In 23rd SPLC, Vol. B. 82:1--82:7.

Digital Library

[23]

Thomas Thüm, Christian Kästner, Fabian Benduhn, Jens Meinicke, Gunter Saake, and Thomas Leich. 2014. FeatureIDE: An extensible framework for feature-oriented software development. Sci. Comput. Program. 79 (2014), 70--85.

Digital Library

Cited By

Sundermann CHeß TSundermann RKuiter EKrieter SThüm T(2024)Generating Feature Models with UVL's Full ExpressivenessProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676602(61-65)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3676602
Galindo JRomero Organvídez DBhushan MHorcas Aguilera JBenavides D(2024)Open Science principles in software product lines: The case of the UVL ecosystemProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3674550(223-223)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3674550
Horcas JGalindo JFuentes LBenavides D(2024)FM Fact LabelScience of Computer Programming10.1016/j.scico.2024.103214(103214)Online publication date: Sep-2024
https://doi.org/10.1016/j.scico.2024.103214
Show More Cited By

Index Terms

FM fact label: a configurable and interactive visualization of feature model characterizations
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines

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Published In

cover image ACM Conferences

SPLC '22: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B

September 2022

246 pages

ISBN:9781450392068

DOI:10.1145/3503229

General Chairs:
Alexander Felfernig
Graz University of Technology, Austria
,
Lidia Fuentes
University of Málaga, Spain

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 September 2022

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MICINN
European Union
Junta de Andalucía

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SPLC '22

Sponsor:

SIGSOFT

SPLC '22: 26th ACM International Systems and Software Product Line Conference

September 12 - 16, 2022

Graz, Austria

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SPLC '22 Paper Acceptance Rate 14 of 41 submissions, 34%;

Overall Acceptance Rate 167 of 463 submissions, 36%

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Cited By

Sundermann CHeß TSundermann RKuiter EKrieter SThüm T(2024)Generating Feature Models with UVL's Full ExpressivenessProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676602(61-65)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3676602
Galindo JRomero Organvídez DBhushan MHorcas Aguilera JBenavides D(2024)Open Science principles in software product lines: The case of the UVL ecosystemProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3674550(223-223)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3674550
Horcas JGalindo JFuentes LBenavides D(2024)FM Fact LabelScience of Computer Programming10.1016/j.scico.2024.103214(103214)Online publication date: Sep-2024
https://doi.org/10.1016/j.scico.2024.103214
Romero-Organvidez DGalindo JSundermann CHorcas JBenavides D(2024)UVLHubJournal of Systems and Software10.1016/j.jss.2024.112150216:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112150
Galindo JDominguez AWhite JBenavides D(2023)Large Language Models to generate meaningful feature model instancesProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume A10.1145/3579027.3608973(15-26)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579027.3608973
Munoz DPinto MFuentes L(2023)Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learningKnowledge-Based Systems10.1016/j.knosys.2023.110558270:COnline publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110558
Munoz DPinto MFuentes LBatory D(2023)Transforming Numerical Feature Models into Propositional Formulas and the Universal Variability LanguageJournal of Systems and Software10.1016/j.jss.2023.111770204:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111770

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