research-article

Development of the Maintainability Index for SPLs Feature Models Using Fuzzy Logic

Authors:

Davi Cedraz S. de Oliveira,

Carla I. M. BezerraAuthors Info & Claims

SBES '19: Proceedings of the XXXIII Brazilian Symposium on Software Engineering

Pages 357 - 366

https://doi.org/10.1145/3350768.3351299

Published: 23 September 2019 Publication History

Abstract

The variability of the common features in an Software Product Line (SPL) can be managed by an feature model, an artifact that consist of a tree-shaped diagram, that describe the features identified in the products and the possible relationships between them. Guarantee the quality of the feature model may be essential to ensure that errors do not propagate across all products. The process of evaluating the quality of a product or artifact can be done using measures, which may reflect the characteristics, sub-characteristics or attributes of quality. However, the isolated values of each measure do not allow access to a whole quality of the feature model, since most of the measures cover several specific aspects that are not correlated. In this context, this paper proposes the aggregation of measures in order to evaluate the maintainability of the feature model in SPL. We aim to investigate how to aggregate these measures and access the respective sub-characteristics by means of a single aggregate value that has the same available information as a set of measures. For this, we have used the theory of Fuzzy Logic as a technique for aggregation of these measures. The new aggregate measure represents the maintainability index of a feature models (MIFM) was obtained. Moreover, to evaluate the MIFM, we applied it to a set of models. It was verified that the aggregate measure obtained allows to measure if a feature models has a high or low maintainability index, supporting the domain engineer in the evaluation of the maintenance of the feature model in a faster and more precise way.

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Cited By

Silva PBezerra CMachado I(2023)Automating Feature Model maintainability evaluation using machine learning techniquesJournal of Systems and Software10.1016/j.jss.2022.111539195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111539
Silva PBezerra CMachado IMousavi MSchobbens P(2021)A machine learning model to classify the feature model maintainabilityProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A10.1145/3461001.3471152(35-45)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3461001.3471152

Index Terms

Development of the Maintainability Index for SPLs Feature Models Using Fuzzy Logic
1. General and reference
  1. Cross-computing tools and techniques
    1. Evaluation
    2. Measurement
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines

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Published In

cover image ACM Other conferences

SBES '19: Proceedings of the XXXIII Brazilian Symposium on Software Engineering

September 2019

583 pages

ISBN:9781450376518

DOI:10.1145/3350768

General Chairs:
Ivan Machado
UFBA
,
Rodrigo Souza
UFBA
,
Rita Suzana Maciel
UFBA
,
Claudio Sant'Anna
UFBA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SBC: Sociedade Brasileira de Computação

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2019

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SBES 2019

SBES 2019: XXXIII Brazilian Symposium on Software Engineering

September 23 - 27, 2019

Salvador, Brazil

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SBES '19 Paper Acceptance Rate 67 of 153 submissions, 44%;

Overall Acceptance Rate 147 of 427 submissions, 34%

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Cited By

Silva PBezerra CMachado I(2023)Automating Feature Model maintainability evaluation using machine learning techniquesJournal of Systems and Software10.1016/j.jss.2022.111539195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111539
Silva PBezerra CMachado IMousavi MSchobbens P(2021)A machine learning model to classify the feature model maintainabilityProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A10.1145/3461001.3471152(35-45)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3461001.3471152

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