A framework based on learning techniques for decision-making in self-adaptive software

Detalhes bibliográficos
Autor(a) principal: Affonso, Frank José [UNESP]
Data de Publicação: 2015
Outros Autores: Leite, Gustavo [UNESP], Oliveira, Rafael A.P., Nakagawa, Elisa Yumi
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.18293/SEKE2015-125
http://hdl.handle.net/11449/178035
Resumo: The development of Self-adaptive Software (SaS) presents specific innovative features compared to traditional ones since this type of software constantly deals with structural and/or behavioral changes at runtime. Capabilities of human administration are showing a decrease in relative effectiveness, since some tasks have been difficult to manage introducing potential problems, such as change management and simple human error. Self-healing systems, a system class of SaS, have emerged as a feasible solution in contrast to management complexity, since such system often combines machine learning techniques with control loops to reduce the number of situations requiring human intervention. This paper presents a framework based on learning techniques and the control loop (MAPE-K) to support the decision-making activity for SaS. In addition, it is noteworthy that this framework is part of a wider project developed by the authors of this paper in previous work (i.e., reference architecture for SaS [1]). Aiming to present the viability of our framework, we have conducted a case study using a flight plan module for Unmanned Aerial Vehicles. The results have shown an environment accuracy of about 80%, enabling us to project good perspectives of contribution to the SaS area and other domains of software systems, and enabling knowledge sharing and technology transfer from academia to industry.
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spelling A framework based on learning techniques for decision-making in self-adaptive softwareDecision-makingFrameworkLearning TechniquesReference ArchitectureSelf-adaptive softwareThe development of Self-adaptive Software (SaS) presents specific innovative features compared to traditional ones since this type of software constantly deals with structural and/or behavioral changes at runtime. Capabilities of human administration are showing a decrease in relative effectiveness, since some tasks have been difficult to manage introducing potential problems, such as change management and simple human error. Self-healing systems, a system class of SaS, have emerged as a feasible solution in contrast to management complexity, since such system often combines machine learning techniques with control loops to reduce the number of situations requiring human intervention. This paper presents a framework based on learning techniques and the control loop (MAPE-K) to support the decision-making activity for SaS. In addition, it is noteworthy that this framework is part of a wider project developed by the authors of this paper in previous work (i.e., reference architecture for SaS [1]). Aiming to present the viability of our framework, we have conducted a case study using a flight plan module for Unmanned Aerial Vehicles. The results have shown an environment accuracy of about 80%, enabling us to project good perspectives of contribution to the SaS area and other domains of software systems, and enabling knowledge sharing and technology transfer from academia to industry.Dept. of Statistics Applied Mathematics and Computation Univ Estadual Paulista-UNESPDept. of Computer Systems University of São Paulo-USPDept. of Statistics Applied Mathematics and Computation Univ Estadual Paulista-UNESPUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Affonso, Frank José [UNESP]Leite, Gustavo [UNESP]Oliveira, Rafael A.P.Nakagawa, Elisa Yumi2018-12-11T17:28:19Z2018-12-11T17:28:19Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject24-29http://dx.doi.org/10.18293/SEKE2015-125Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, v. 2015-January, p. 24-29.2325-90862325-9000http://hdl.handle.net/11449/17803510.18293/SEKE2015-1252-s2.0-84969800064Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE0,157info:eu-repo/semantics/openAccess2021-10-23T21:44:27Zoai:repositorio.unesp.br:11449/178035Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:02:40.516232Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A framework based on learning techniques for decision-making in self-adaptive software
title A framework based on learning techniques for decision-making in self-adaptive software
spellingShingle A framework based on learning techniques for decision-making in self-adaptive software
Affonso, Frank José [UNESP]
Decision-making
Framework
Learning Techniques
Reference Architecture
Self-adaptive software
title_short A framework based on learning techniques for decision-making in self-adaptive software
title_full A framework based on learning techniques for decision-making in self-adaptive software
title_fullStr A framework based on learning techniques for decision-making in self-adaptive software
title_full_unstemmed A framework based on learning techniques for decision-making in self-adaptive software
title_sort A framework based on learning techniques for decision-making in self-adaptive software
author Affonso, Frank José [UNESP]
author_facet Affonso, Frank José [UNESP]
Leite, Gustavo [UNESP]
Oliveira, Rafael A.P.
Nakagawa, Elisa Yumi
author_role author
author2 Leite, Gustavo [UNESP]
Oliveira, Rafael A.P.
Nakagawa, Elisa Yumi
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Affonso, Frank José [UNESP]
Leite, Gustavo [UNESP]
Oliveira, Rafael A.P.
Nakagawa, Elisa Yumi
dc.subject.por.fl_str_mv Decision-making
Framework
Learning Techniques
Reference Architecture
Self-adaptive software
topic Decision-making
Framework
Learning Techniques
Reference Architecture
Self-adaptive software
description The development of Self-adaptive Software (SaS) presents specific innovative features compared to traditional ones since this type of software constantly deals with structural and/or behavioral changes at runtime. Capabilities of human administration are showing a decrease in relative effectiveness, since some tasks have been difficult to manage introducing potential problems, such as change management and simple human error. Self-healing systems, a system class of SaS, have emerged as a feasible solution in contrast to management complexity, since such system often combines machine learning techniques with control loops to reduce the number of situations requiring human intervention. This paper presents a framework based on learning techniques and the control loop (MAPE-K) to support the decision-making activity for SaS. In addition, it is noteworthy that this framework is part of a wider project developed by the authors of this paper in previous work (i.e., reference architecture for SaS [1]). Aiming to present the viability of our framework, we have conducted a case study using a flight plan module for Unmanned Aerial Vehicles. The results have shown an environment accuracy of about 80%, enabling us to project good perspectives of contribution to the SaS area and other domains of software systems, and enabling knowledge sharing and technology transfer from academia to industry.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2018-12-11T17:28:19Z
2018-12-11T17:28:19Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.18293/SEKE2015-125
Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, v. 2015-January, p. 24-29.
2325-9086
2325-9000
http://hdl.handle.net/11449/178035
10.18293/SEKE2015-125
2-s2.0-84969800064
url http://dx.doi.org/10.18293/SEKE2015-125
http://hdl.handle.net/11449/178035
identifier_str_mv Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, v. 2015-January, p. 24-29.
2325-9086
2325-9000
10.18293/SEKE2015-125
2-s2.0-84969800064
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
0,157
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24-29
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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