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Locating Causes of Inconsistency in a Variability Model for Software Product Line
by
Lee, Jihyun
, Han, Younghun
, Kang, Sungwon
in
Backup software
/ inconsistency cause locating
/ inconsistency in a variability model
/ Logic
/ Product introduction
/ Product lines
/ Quantitative analysis
/ Software
/ software product line
/ variability modeling
2025
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Locating Causes of Inconsistency in a Variability Model for Software Product Line
by
Lee, Jihyun
, Han, Younghun
, Kang, Sungwon
in
Backup software
/ inconsistency cause locating
/ inconsistency in a variability model
/ Logic
/ Product introduction
/ Product lines
/ Quantitative analysis
/ Software
/ software product line
/ variability modeling
2025
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Do you wish to request the book?
Locating Causes of Inconsistency in a Variability Model for Software Product Line
by
Lee, Jihyun
, Han, Younghun
, Kang, Sungwon
in
Backup software
/ inconsistency cause locating
/ inconsistency in a variability model
/ Logic
/ Product introduction
/ Product lines
/ Quantitative analysis
/ Software
/ software product line
/ variability modeling
2025
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Locating Causes of Inconsistency in a Variability Model for Software Product Line
Journal Article
Locating Causes of Inconsistency in a Variability Model for Software Product Line
2025
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Overview
One of the central activities of software product line development is variability modeling for a product family. Because variability models are needed at various stages of software product line development, determining whether a variability model has been modeled correctly is an essential activity for successful software product line development. Existing studies proposed various methods for analysis of various aspects of correctness of a variability model. In particular, analyzing whether a variability model is consistent or not is considered the most important analysis perspective since it is impossible to configure products from such a model. There are few studies in the software product line field that locate causes of inconsistency in a variability model. Furthermore, these existing methods cannot locate the exact causes of inconsistency due to the fact that the feature model they are based on allows ambiguity in its parent–child relationship or due to the fact that they are designed to produce explanations rather than locations of causes, resulting in producing long and complex explanations as the size of the feature model increases. In this work, we propose a method that determines whether or not a variability model has an inconsistency and identifies the exact locations of its causes if it has an inconsistency. To evaluate the proposed method, we developed a tool that automatically performs all the steps of the method and used it to conduct experiments with 49 models, including real-world variability models. As a result, the proposed method accurately identified all models with an inconsistency and located all causes of inconsistency in them.
Publisher
MDPI AG
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