Defining categorical reasoning of numerical feature models with feature-wise and variant-wise quality attributes
Pages 132 - 139
Abstract
Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.
In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE - the state-of-the-art CT tool.
References
[1]
Michael Barr and Charles Wells. 1990. Category theory for computing science. Prentice Hall, Hoboken, New Jersey, USA.
[2]
Rabih Bashroush, Muhammad Garba, Rick Rabiser, Iris Groher, and Goetz Botterweck. 2017. CASE Tool Support for Variability Management in SPLs. ACM Comput. Surv. 50, 1, Article 14 (March 2017), 45 pages.
[3]
David Benavides, Sergio Segura, and Antonio Ruiz-Cortés. 2010. Automated analysis of feature models 20 years later: A literature review. Information Systems 35, 6 (2010), 615 -- 636.
[4]
David Benavides, Pablo Trinidad, and Antonio Ruiz-Cortés. 2005. Automated Reasoning on Feature Models. In Advanced Information Systems Engineering, Oscar Pastor and João Falcão e Cunha (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 491--503.
[5]
Mateus Borges, Quoc-Sang Phan, Antonio Filieri, and Corina S. Păsăreanu. 2017. Model-Counting Approaches for Nonlinear Numerical Constraints. In NASA Formal Methods, Clark Barrett, Misty Davies, and Temesghen Kahsai (Eds.). Springer International Publishing, Luxembourg, 131--138.
[6]
Kristopher S Brown, David I Spivak, and Ryan Wisnesky. 2019. Categorical data integration for computational science. Computational Materials Science 164 (2019), 127--132.
[7]
Lianping Chen, Muhammad Ali Babar, and Nour Ali. 2009. Variability Management in Software Product Lines: A Systematic Review. In Proceedings of the 13th International Software Product Line Conference (San Francisco, California, USA) (SPLC '09). Carnegie Mellon University, USA, 81--90.
[8]
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 Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B (SPLC '20). ACM, New York, NY, USA, 52--55.
[9]
M. Glinz. 2007. On Non-Functional Requirements. In 15th IEEE International Requirements Engineering Conference (RE 2007). IEEE, Delhi, India, 21--26.
[10]
Jianmei Guo, Jia Hui Liang, Kai Shi, Dingyu Yang, Jingsong Zhang, Krzysztof Czarnecki, Vijay Ganesh, and Huiqun Yu. 2019. SMTIBEA: a hybrid multi-objective optimization algorithm for configuring large constrained software product lines. Software & Systems Modeling 18, 2 (2019), 1447--1466.
[11]
Dilian Gurov, Bjarte M Østvold, and Ina Schaefer. 2011. A hierarchical variability model for software product lines. In International Symposium On Leveraging Applications of Formal Methods, Verification and Validation. Springer, Springer, Luxembourg, 181--199.
[12]
John N. Hooker. 2002. Logic, Optimization, and Constraint Programming. INFORMS Journal on Computing 14, 4 (2002), 295--321.
[13]
Jose-Miguel Horcas, Mónica Pinto, and Lidia Fuentes. 2016. An automatic process for weaving functional quality attributes using a software product line approach. Journal of Systems and Software 112 (2016), 78 -- 95.
[14]
Jose-Miguel Horcas, Mónica Pinto, and Lidia Fuentes. 2019. Software Product Line Engineering: A Practical Experience. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A (Paris, France) (SPLC '19). ACM, New York, New York, USA, 164--176.
[15]
C. Kaltenecker, A. Grebhahn, N. Siegmund, and S. Apel. 2020. The Interplay of Sampling and Machine Learning for Software Performance Prediction. IEEE Software 37, 4 (2020), 58--66.
[16]
Kyo C Kang, Sholom G Cohen, James A Hess, William E Novak, and A Spencer Peterson. 1990. Feature-oriented domain analysis (FODA) feasibility study. Technical Report. Carnegie-Mellon Univ Pittsburgh Pa Software Engineering Inst.
[17]
Georg P Loczewski. 2018. A++ and the Lambda Calculus: Principles of Functional Programming. tredition, Berlin, Germany.
[18]
Jens Meinicke, Thomas Thüm, Reimar Schröter, Fabian Benduhn, Thomas Leich, and Gunter Saake. 2017. Quality Assurance for Feature Models and Configurations. Springer International Publishing, Cham, 81--94.
[19]
Daniel-Jesus Munoz, Dilian Gurov, Monica Pinto, and Lidia Fuentes. 2021. Category Theory Framework for Variability Models with Non-functional Requirements. In Advanced Information Systems Engineering, Marcello La Rosa, Shazia Sadiq, and Ernest Teniente (Eds.). Springer International Publishing, Cham, 397--413.
[20]
Daniel-Jesus Munoz, Jeho Oh, Mónica Pinto, Lidia Fuentes, and Don Batory. 2019. Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A (Paris, France). ACM, New York, New York, USA, 289--301.
[21]
Daniel-Jesus Munoz, Mónica Pinto, and Lidia Fuentes. 2018. Finding correlations of features affecting energy consumption and performance of web servers using the HADAS eco-assistant. Computing 100, 11 (2018), 1155--1173.
[22]
Lina Ochoa, Juliana Alves Pereira, Oscar González-Rojas, Harold Castro, and Gunter Saake. 2017. A Survey on Scalability and Performance Concerns in Extended Product Lines Configuration. In Proceedings of the 11th Int. Workshop on VAMOS'2017 (Eindhoven, Netherlands). ACM, New York, New York, USA, 5--12.
[23]
Rafael Olaechea, Steven Stewart, Krzysztof Czarnecki, and Derek Rayside. 2012. Modelling and Multi-Objective Optimization of Quality Attributes in Variability-Rich Software. In Proceedings of the Fourth International Workshop on Nonfunctional System Properties in Domain Specific Modeling Languages (Innsbruck, Austria). ACM, New York, New York, USA, Article 2, 6 pages.
[24]
Juliana Alves Pereira, Mathieu Acher, Hugo Martin, Jean-Marc Jézéquel, Goetz Botterweck, and Anthony Ventresque. 2021. Learning software configuration spaces: A systematic literature review. Journal of Systems and Software 182 (2021), 111044.
[25]
Klaus Pohl, Günter Böckle, and Frank J van Der Linden. 2005. Software product line engineering: foundations, principles and techniques. Springer Science & Business Media, Luxembourg.
[26]
Norbert Siegmund, Marko Rosenmüller, Martin Kuhlemann, Christian Kästner, Sven Apel, and Gunter Saake. 2012. SPL Conqueror: Toward Optimization of Non-Functional Properties in Software Product Lines. Software Quality Journal 20, 3--4 (sep 2012), 487--517.
[27]
Norbert Siegmund, Stefan Sobernig, and Sven Apel. 2017. Attributed Variability Models: Outside the Comfort Zone. In Proceedings of the 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017). ACM, New York, New York, USA, 268--278.
[28]
David I Spivak and Robert E Kent. 2012. Ologs: a categorical framework for knowledge representation. PloS one 7, 1 (2012), e24274.
[29]
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 Proceedings of the 25th ACM International Systems and Software Product Line Conference-Volume A. ACM, New York, NY, USA, 136--147.
[30]
Maurice H. ter Beek and Axel Legay. 2019. Quantitative Variability Modeling and Analysis. In Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems (Leuven, Belgium) (VAMOS '19). ACM, New York, New York, USA, Article 13, 2 pages.
[31]
Lanxin Yang, He Zhang, Haifeng Shen, Xin Huang, Xin Zhou, Guoping Rong, and Dong Shao. 2021. Quality Assessment in Systematic Literature Reviews: A Software Engineering Perspective. Information and Software Technology 130 (2021), 106397.
[32]
Anton Yrjönen and Janne Merilinna. 2009. Extending the NFR framework with measurable non-functional requirements. In Proceedings of the 2nd International Workshop on Non-functional System Properties in Domain Specific Modeling Languages, Marko Boškoviæ, Dragan Gaševiæ, Claus Pahl, and Bernhard Schätz (Eds.). ACM, New York, New York, USA, 0--14. 2nd International Workshop on Non-functional System Properties in Domain Specific Modeling Languages, NFPinDSML2009, NFPinDSML2009; Conference date: 04-10-2009 Through 04-10-2009.
[33]
Guoheng Zhang, Huilin Ye, and Yuqing Lin. 2014. Quality attribute modeling and quality aware product configuration in software product lines. Softw. Qual. J. 22, 3 (2014), 365--401.
Index Terms
- Defining categorical reasoning of numerical feature models with feature-wise and variant-wise quality attributes
Recommendations
Quality Attributes Assessment for Feature-Based Product Configuration in Software Product Line
APSEC '10: Proceedings of the 2010 Asia Pacific Software Engineering ConferenceProduct configuration based on a feature model in software product lines is the process of selecting the desired features based on customers’ requirements. In most cases, application engineers focus on the functionalities of the target product during ...
Category Theory Framework for Variability Models with Non-functional Requirements
Advanced Information Systems EngineeringAbstractIn Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those ...
Mapping extended feature models to constraint logic programming over finite domains
SPLC'10: Proceedings of the 14th international conference on Software product lines: going beyondAs feature models for realistic product families may be quite complicated, automated analysis of feature models is desirable. Although several approaches reported in the literature addressed this issue, complex featureattribute and attribute-attribute ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
September 2022
246 pages
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]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 September 2022
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- European Union's H2020 research and innovation programme
- Ministerio de Ciencia e Innovación
- European Union FEDER
- Junta de Andalucía
Conference
SPLC '22
Sponsor:
SPLC '22: 26th ACM International Systems and Software Product Line Conference
September 12 - 16, 2022
Graz, Austria
Acceptance Rates
SPLC '22 Paper Acceptance Rate 14 of 41 submissions, 34%;
Overall Acceptance Rate 167 of 463 submissions, 36%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 65Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in