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ToffA-DSPL: An approach of Trade-off Analysis for designing Dynamic Software Product Lines
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  • Michelle Larissa Luciano Carvalho,
  • Ismayle de Sousa Santos,
  • Paulo Masiero,
  • Tiago Januario,
  • Pedro Neto,
  • Rossana Maria de Castro Andrade,
  • Eduardo Almeida
Michelle Larissa Luciano Carvalho
Universidade Federal da Bahia Departamento de Ciencia da Computacao

Corresponding Author:[email protected]

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Ismayle de Sousa Santos
Universidade Estadual do Ceara
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Paulo Masiero
Universidade de Sao Paulo Campus de Sao Carlos
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Tiago Januario
Universidade Federal da Bahia Departamento de Ciencia da Computacao
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Pedro Neto
Universidade Federal do Piaui Centro de Tecnologia
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Rossana Maria de Castro Andrade
Universidade Federal do Ceara
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Eduardo Almeida
Universidade Federal da Bahia Departamento de Ciencia da Computacao
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Software engineers have adopted the Dynamic Software Product Lines (DSPL) engineering practices to develop Dynamically Adaptable Software (DAS). DAS is seen as a DSPL application and must cope with a large number of configurations of features, Non-functional Requirements (NFRs), and contexts. However, the accurate representation of the impact of features over NFRs and contexts for the identification of optimal configurations is not a trivial task. Software engineers need to have domain knowledge and design DAS before deploying to satisfy those requirements. Aiming to handle them, we proposed an approach of Trade-off Analysis for DSPL at design-time, named ToffA-DSPL. It deals with the configuration selection process considering interactions between NFRs and contexts. We performed an exploratory study based on simulations to identify the usefulness of the ToffA-DSPL approach. In general, the configurations suggested by ToffA-DSPL provide high satisfaction levels of NFRs. Based on simulations, we evidenced that our approach aims to explore reuse and is useful for generating valid and optimal configurations. In addition, ToffA-DSPL enables software engineers to conduct trade-off analysis, evaluate changes in the context feature, and define an adaptation model from optimal configurations found in the analysis.