ObasCId(-Tool): an ontologically based approach for concern identification and classification and its computational support
The aspect-oriented requirements engineering (AORE) area intends to provide more appropriated strategies for software concern identification, classification (as crosscutting or non-crosscutting), and modularization, in the early phases of software development cycle. A commonly reported issue about t...
| Autores: | ; |
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| Tipo de documento: | Artigo |
| Estado: | Published version |
| Data de publicação: | 2018 |
| País: | Brasil |
| Recursos: | UFLA |
| Repositório: | Repositório Institucional da UFLA |
| Idioma: | Inglês |
| OAI Identifier: | oai:repositorio.ufla.br:1/34626 |
| Acesso em linha: | https://repositorio.ufla.br/handle/1/34626 |
| Nível de acesso: | Acesso aberto |
| Palavra-chave: | Crosscutting concerns Early-Aspects Aspect-oriented requirements engineering Concern identification and classification |
| Resumo: | The aspect-oriented requirements engineering (AORE) area intends to provide more appropriated strategies for software concern identification, classification (as crosscutting or non-crosscutting), and modularization, in the early phases of software development cycle. A commonly reported issue about the existing AORE approaches is the lack of appropriated resources (guidelines, processes, catalogs, among others) to support software engineers during the concern identification and classification. This work aims to mitigate this issue by proposing (i) a reference ontology for the software concern domain, called O4C (Ontology for Concerns); (ii) an ontologically based approach for AORE, called ObasCId, that suggests the usage of catalogs of software concerns and a well-defined process for supporting software engineers to perform these activities in a more systematic way; and (iii) a computational support, called ObasCId-Tool, that automates some activities of the ObasCId. Two quasi-experimental studies were performed on ObasCId and ObasCId-Tool, and their results indicated that these technologies may positively contribute for the concern identification and classification effectiveness without harming its execution time. |
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