Locating Causes of Inconsistency in a Variability Model for Software Product Line

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.

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Publication Details

Journal
Applied Sciences
Published
2025-11-20
DOI
https://doi.org/10.3390/app152212328
Primary Topic
Advanced Software Engineering Methodologies
Type
article
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article

Locating Causes of Inconsistency in a Variability Model for Software Product Line

Sungwon Kang, Younghun Han, Jihyun Lee
Applied Sciences
Advanced Software Engineering Methodologies
article

Locating Causes of Inconsistency in a Variability Model for Software Product Line

Sungwon Kang, Younghun Han, Jihyun Lee
article en

Abstract

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.

Applied SciencesVol. 15(22)
Korea Advanced Institute of Science and Technology (KR), Jeonbuk National University (KR)
Openalex Percentile: Top 81%
Advanced Software Engineering Methodologies
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