Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code

Autores
Abait, Esteban S.; Marcos, Claudia A.
Año de publicación
2010
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Although aspect mining techniques are supposed to help software developers in discovering where crosscutting concerns are located in source code, in practice, the amount of user involvement required hinder their applicability. The large number of yielded candidates in conjunction with the exhibition of low precision and low recall constitutes the main pitfalls that current aspect mining techniques suffer from. In order to overcome the aforementioned problems we propose to restate the aspect mining problem as a decision making problem. If each aspect mining technique is considered to be an expert on its own, the combination of multiple expert judgments is supposed to improve the effectiveness of the identification process. The proposed approach uses several aspect mining algorithms whose results are combined using a technique known as linear opinion pool. A linear opinion pool is a mathematical combination method commonly used in decision making for aggregating the opinions of several experts in a given area. The output of the proposed approach is a ranking of source code elements (candidate seeds) that may correspond to a crosscutting concern. Our main hypothesis are: (1) the application of decision making techniques reduces the number of generated candidate seeds while improving the precision, and (2) by combining techniques based on different program analysis techniques (as static analysis or dynamic analysis) the recall can be improved. Preliminary results shows the viability of the approach and the validity of our claims.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
aspect mining
decision making
crosscutting concerns
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/153051

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spelling Improving Effectiveness in the Identification of Crosscutting Concerns in Source CodeAbait, Esteban S.Marcos, Claudia A.Ciencias Informáticasaspect miningdecision makingcrosscutting concernsAlthough aspect mining techniques are supposed to help software developers in discovering where crosscutting concerns are located in source code, in practice, the amount of user involvement required hinder their applicability. The large number of yielded candidates in conjunction with the exhibition of low precision and low recall constitutes the main pitfalls that current aspect mining techniques suffer from. In order to overcome the aforementioned problems we propose to restate the aspect mining problem as a decision making problem. If each aspect mining technique is considered to be an expert on its own, the combination of multiple expert judgments is supposed to improve the effectiveness of the identification process. The proposed approach uses several aspect mining algorithms whose results are combined using a technique known as linear opinion pool. A linear opinion pool is a mathematical combination method commonly used in decision making for aggregating the opinions of several experts in a given area. The output of the proposed approach is a ranking of source code elements (candidate seeds) that may correspond to a crosscutting concern. Our main hypothesis are: (1) the application of decision making techniques reduces the number of generated candidate seeds while improving the precision, and (2) by combining techniques based on different program analysis techniques (as static analysis or dynamic analysis) the recall can be improved. Preliminary results shows the viability of the approach and the validity of our claims.Sociedad Argentina de Informática e Investigación Operativa2010info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionResumenhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf612-612http://sedici.unlp.edu.ar/handle/10915/153051enginfo:eu-repo/semantics/altIdentifier/url/http://39jaiio.sadio.org.ar/sites/default/files/39jaiio-asse-32.pdfinfo:eu-repo/semantics/altIdentifier/issn/1850-2792info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:11:24Zoai:sedici.unlp.edu.ar:10915/153051Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:11:24.463SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
title Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
spellingShingle Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
Abait, Esteban S.
Ciencias Informáticas
aspect mining
decision making
crosscutting concerns
title_short Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
title_full Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
title_fullStr Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
title_full_unstemmed Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
title_sort Improving Effectiveness in the Identification of Crosscutting Concerns in Source Code
dc.creator.none.fl_str_mv Abait, Esteban S.
Marcos, Claudia A.
author Abait, Esteban S.
author_facet Abait, Esteban S.
Marcos, Claudia A.
author_role author
author2 Marcos, Claudia A.
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
aspect mining
decision making
crosscutting concerns
topic Ciencias Informáticas
aspect mining
decision making
crosscutting concerns
dc.description.none.fl_txt_mv Although aspect mining techniques are supposed to help software developers in discovering where crosscutting concerns are located in source code, in practice, the amount of user involvement required hinder their applicability. The large number of yielded candidates in conjunction with the exhibition of low precision and low recall constitutes the main pitfalls that current aspect mining techniques suffer from. In order to overcome the aforementioned problems we propose to restate the aspect mining problem as a decision making problem. If each aspect mining technique is considered to be an expert on its own, the combination of multiple expert judgments is supposed to improve the effectiveness of the identification process. The proposed approach uses several aspect mining algorithms whose results are combined using a technique known as linear opinion pool. A linear opinion pool is a mathematical combination method commonly used in decision making for aggregating the opinions of several experts in a given area. The output of the proposed approach is a ranking of source code elements (candidate seeds) that may correspond to a crosscutting concern. Our main hypothesis are: (1) the application of decision making techniques reduces the number of generated candidate seeds while improving the precision, and (2) by combining techniques based on different program analysis techniques (as static analysis or dynamic analysis) the recall can be improved. Preliminary results shows the viability of the approach and the validity of our claims.
Sociedad Argentina de Informática e Investigación Operativa
description Although aspect mining techniques are supposed to help software developers in discovering where crosscutting concerns are located in source code, in practice, the amount of user involvement required hinder their applicability. The large number of yielded candidates in conjunction with the exhibition of low precision and low recall constitutes the main pitfalls that current aspect mining techniques suffer from. In order to overcome the aforementioned problems we propose to restate the aspect mining problem as a decision making problem. If each aspect mining technique is considered to be an expert on its own, the combination of multiple expert judgments is supposed to improve the effectiveness of the identification process. The proposed approach uses several aspect mining algorithms whose results are combined using a technique known as linear opinion pool. A linear opinion pool is a mathematical combination method commonly used in decision making for aggregating the opinions of several experts in a given area. The output of the proposed approach is a ranking of source code elements (candidate seeds) that may correspond to a crosscutting concern. Our main hypothesis are: (1) the application of decision making techniques reduces the number of generated candidate seeds while improving the precision, and (2) by combining techniques based on different program analysis techniques (as static analysis or dynamic analysis) the recall can be improved. Preliminary results shows the viability of the approach and the validity of our claims.
publishDate 2010
dc.date.none.fl_str_mv 2010
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dc.language.none.fl_str_mv eng
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info:eu-repo/semantics/altIdentifier/issn/1850-2792
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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