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Dynamic decision-making based on NFR for managing software variability and configuration selection

Authors:

André Almeida,

Everton Cavalcante,

Francisco DantasAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 1376 - 1382

https://doi.org/10.1145/2695664.2695875

Published: 13 April 2015 Publication History

Abstract

Due to dynamic variability, identifying the specific conditions under which non-functional requirements (NFRs) are satisfied may be only possible at runtime. Therefore, it is necessary to consider the dynamic treatment of relevant information during the requirements specifications. The associated data can be gathered by monitoring the execution of the application and its underlying environment to support reasoning about how the current application configuration is fulfilling the established requirements. This paper presents a dynamic decision-making infrastructure to support both NFRs representation and monitoring, and to reason about the degree of satisfaction of NFRs during runtime. The infrastructure is composed of: (i) an extended feature model aligned with a domain-specific language for representing NFRs to be monitored at runtime; (ii) a monitoring infrastructure to continuously assess NFRs at runtime; and (iii) a flexible decision-making process to select the best available configuration based on the satisfaction degree of the NRFs. The evaluation of the approach has shown that it is able to choose application configurations that well fit user NFRs based on runtime information. The evaluation also revealed that the proposed infrastructure provided consistent indicators regarding the best application configurations that fit user NFRs. Finally, a benefit of our approach is that it allows us to quantify the level of satisfaction with respect to NFRs specification.

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Cited By

Dongmo C(2024)A Review of Non-Functional Requirements Analysis Throughout the SDLCComputers10.3390/computers1312030813:12(308)Online publication date: 23-Nov-2024
https://doi.org/10.3390/computers13120308
Han DMa GCai YWang BLi A(2022)ProbaSAS: Modeling and Decision-Making Approach for Self-Adaptive Software Systems under Uncertainty2022 41st Chinese Control Conference (CCC)10.23919/CCC55666.2022.9901985(5871-5876)Online publication date: 25-Jul-2022
https://doi.org/10.23919/CCC55666.2022.9901985
Han DYang QXing JMa G(2020)EasyModel: A Refinement-Based Modeling and Verification Approach for Self-Adaptive SoftwareJournal of Computer Science and Technology10.1007/s11390-020-0499-x35:5(1016-1046)Online publication date: 30-Sep-2020
https://doi.org/10.1007/s11390-020-0499-x
Show More Cited By

Index Terms

Dynamic decision-making based on NFR for managing software variability and configuration selection
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Requirements analysis
    2. Software development techniques
      1. Reusability
  2. Software notations and tools
    1. Software libraries and repositories

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Information

Published In

cover image ACM Conferences

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2015 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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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

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SAC 2015

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SIGAPP

SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

Acceptance Rates

SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Dongmo C(2024)A Review of Non-Functional Requirements Analysis Throughout the SDLCComputers10.3390/computers1312030813:12(308)Online publication date: 23-Nov-2024
https://doi.org/10.3390/computers13120308
Han DMa GCai YWang BLi A(2022)ProbaSAS: Modeling and Decision-Making Approach for Self-Adaptive Software Systems under Uncertainty2022 41st Chinese Control Conference (CCC)10.23919/CCC55666.2022.9901985(5871-5876)Online publication date: 25-Jul-2022
https://doi.org/10.23919/CCC55666.2022.9901985
Han DYang QXing JMa G(2020)EasyModel: A Refinement-Based Modeling and Verification Approach for Self-Adaptive SoftwareJournal of Computer Science and Technology10.1007/s11390-020-0499-x35:5(1016-1046)Online publication date: 30-Sep-2020
https://doi.org/10.1007/s11390-020-0499-x
Dell’Anna DDalpiaz FDastani M(2019)Requirements-driven evolution of sociotechnical systems via probabilistic reasoning and hill climbingAutomated Software Engineering10.1007/s10515-019-00255-5Online publication date: 22-Apr-2019
https://doi.org/10.1007/s10515-019-00255-5
Rossi DPoggi FCiancarini PHaddad HWainwright RChbeir R(2018)Dynamic high-level requirements in self-adaptive systemsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167143(128-137)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167143
Štuikys VBurbaitė RŠtuikys VBurbaitė R(2018)Challenges of STEM-Driven Computer Science (CS) EducationSmart STEM-Driven Computer Science Education10.1007/978-3-319-78485-4_1(3-29)Online publication date: 29-Jun-2018
https://doi.org/10.1007/978-3-319-78485-4_1
Condori-Fernandez NLopez FNovielli NBegel AMaalej W(2017)Using emotions to empower the self-adaptation capability of software servicesProceedings of the 2nd International Workshop on Emotion Awareness in Software Engineering10.5555/3105556.3105560(15-21)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.5555/3105556.3105560
Rossi DPoggi FCiancarini P(2017)Dynamic high-level in self-adaptive systems2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2017.8342398(49-60)Online publication date: Sep-2017
https://doi.org/10.1109/ICRITO.2017.8342398
Venčkauskas AŠtuikys VJusas NBurbaitė R(2016)Model-Driven Approach for Body Area Network Application DevelopmentSensors10.3390/s1605067016:5(670)Online publication date: 12-May-2016
https://doi.org/10.3390/s16050670
Vilela JGoncalves EHolanda ACastro JFigueiredo B(2016)A retrospective analysis of SAC requirementsACM SIGAPP Applied Computing Review10.1145/2993231.299323416:2(26-41)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.1145/2993231.2993234

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