research-article

Comparing Configuration Approaches for Dynamic Software Product Lines

Authors:

Gabriela Guedes,

Monique SoaresAuthors Info & Claims

SBES '17: Proceedings of the XXXI Brazilian Symposium on Software Engineering

Pages 134 - 143

https://doi.org/10.1145/3131151.3131162

Published: 20 September 2017 Publication History

Abstract

Dynamic Software Product Lines (DSPLs) are Software Product Lines (SPLs) in which the configuration may occur at runtime. DSPL approaches provide means for modeling variability as well as configuring the product according to its runtime context and/or non-functional requirements (NFRs) satisfaction. In this paper, we present a Requirements Engineering (RE) approach for DSPL, ConG4DaS (Contextual Goal models For Dynamic Software product lines), which provides: (i) models for capturing variability with goals, NFRs, contexts and the relationship between them; and (ii) a configuration process that takes contexts, NFRs and their priority and interactions into account. We have used simulation based assessment to compare ConG4DaS with another approach, REFAS (Requirements Engineering For self-Adaptive Software systems), with respect to the satisfaction level of the highest priority softgoal. For the comparison, we modeled two DSPL examples and simulated different scenarios where reconfiguration is necessary. Next, we compared the configurations selected by the approaches with respect to overall NFRs' satisfaction. The results showed that ConG4DaS, which uses utility function in the configuration process, selects configurations that better satisfy NFRs compared to REFAS, which uses constraint programming.

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Cited By

Aguayo OSepúlveda SMazo R(2024)Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line ApproachElectronics10.3390/electronics1305090513:5(905)Online publication date: 27-Feb-2024
https://doi.org/10.3390/electronics13050905
Aguayo OSepúlveda S(2022)Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic MappingApplied Sciences10.3390/app12201024012:20(10240)Online publication date: 12-Oct-2022
https://doi.org/10.3390/app122010240
Brings JDaun MWeyer TPohl K(2020)Analyzing goal variability in cyber-physical system networksACM SIGAPP Applied Computing Review10.1145/3412816.341281820:2(19-35)Online publication date: 27-Jul-2020
https://dl.acm.org/doi/10.1145/3412816.3412818
Show More Cited By

Index Terms

Comparing Configuration Approaches for Dynamic Software Product Lines
1. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Requirements analysis
    2. Software development techniques
      1. Reusability
        Software product lines

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cover image ACM Other conferences

SBES '17: Proceedings of the XXXI Brazilian Symposium on Software Engineering

September 2017

409 pages

ISBN:9781450353267

DOI:10.1145/3131151

Program Chairs:
José Carlos Maldonado
ICMC-USP
,
Fabiano Cutigi Ferrari
UFSCar
,
Uirá Kulesza
DIMAp/UFRN
,
Tayana Uchôa Conte
UFAM

Copyright © 2017 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]

In-Cooperation

SBC: Brazilian Computer Society
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2017

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SBES'17

SBES'17: 31st Brazilian Symposium on Software Engineering

September 20 - 22, 2017

CE, Fortaleza, Brazil

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SBES '17 Paper Acceptance Rate 42 of 134 submissions, 31%;

Overall Acceptance Rate 147 of 427 submissions, 34%

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Cited By

Aguayo OSepúlveda SMazo R(2024)Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line ApproachElectronics10.3390/electronics1305090513:5(905)Online publication date: 27-Feb-2024
https://doi.org/10.3390/electronics13050905
Aguayo OSepúlveda S(2022)Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic MappingApplied Sciences10.3390/app12201024012:20(10240)Online publication date: 12-Oct-2022
https://doi.org/10.3390/app122010240
Brings JDaun MWeyer TPohl K(2020)Analyzing goal variability in cyber-physical system networksACM SIGAPP Applied Computing Review10.1145/3412816.341281820:2(19-35)Online publication date: 27-Jul-2020
https://dl.acm.org/doi/10.1145/3412816.3412818
Brings JDaun MWeyer TPohl KHung CCerny TShin DBechini A(2020)Goal-based configuration analysis for networks of collaborative cyber-physical systemsProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3374011(1387-1396)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3374011
Carvalho MSantos IMasiero PJanuario Tde Almeida Ede Alcântara dos Santos Neto Pde Castro Andrade RLopez-Herrejon R(undefined)Toffa-Das: Design-Time Trade-Off Analysis for Dynamically Adaptable SoftwareSSRN Electronic Journal10.2139/ssrn.4098743
https://doi.org/10.2139/ssrn.4098743

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