A framework based on learning techniques for decision-making in self-adaptive software
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , |
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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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 |
|
_version_ |
1808129386393632768 |