• Journal of Software Engineering Society
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• v.26 no.2
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• pp.31-44
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• 2013

Software product line (SPL) engineering is a reuse paradigm that helps organizations increase productivity and improve product quality by developing product from reusable core assets. In SPL, product configuration is the process of selecting the desired features and feature attributes for a given product from a feature model. In order to develop a successful product, feature and feature attribute selection that can achieve the product goal is important. There can be thousands of features and feature attributes resulting in myriads of configurations and finding the best configuration efficiently is a hard task. This paper proposes a systematic process for feature-based product configuration. To support development of a product that satisfys all product goals(business goals and quality goals), a model showing how feature and feature attribute combinations are related to product goals is included and a method for deriving an optimal product configuration using the model is proposed.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.119-127
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• 2005

Product line analysis is an activity for analyzing requirements, their relationships, and constraints in a product line before engineering product line assets (e.g., architectures and components). A feature-oriented commonality and variability analysis (called feature modeling) has been considered an essential part of product line analysis. Commonality and variability analysis, although critical, is not sufficient to develop reusable and adaptable product line assets. Dependencies among features and feature binding time also have significant influences on the design of product line assets. In this paper. we propose a feature-oriented product line analysis model that extends the existing feature model in terms of three aspects (i.e., feature commonality and variability, feature dependency, and feature binding time). To validate the consistency among the three aspects we formally define the feature-oriented product line analysis model and provide rules for checking consistency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.115-120
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• 2014

Many product line engineering methods use the feature model to structure commonality and variability among products in terms of features and to derive a product feature configuration, which is the set of features required for the development of a product. Features to be selected during product derivation are mainly determined based on the quality attributes required for a product. Most methods published so far derived an optimal product feature configuration through linear co-relationship between features and quality attributes. However, the co-relationship between features and quality attributes can be formulated as a non-linear function because of feature interactions. This paper proposes a method that derives an optimal product feature configuration considering feature interactions. Four product line cases are used to validate the proposed methods.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.135-144
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• 2007

Feature models are widely used in domain engineering phase of software product lines development to model the common and variable concepts among products. From the feature model, the feature configurations are generated by selecting the features to be included in target product. The feature configuration represents the requirements for the specific product to be implemented. Although there are a lot of researches on how to build and use the feature models and feature configurations, the researches on the formal semantics and reasoning of them are rather inactive. This paper proposes the feature configuration verification approach based on JESS, java-based rule-base system. The Graph Product Line, a standard problem for evaluating the software product line technologies, is used throughout the paper to illustrate this approach. The approach in this paper has advantage of presenting the exact reason causing inconsistency in the feature configuration. In addition, this approach should be easily applied into other software product lines development environments because JESS system can be easily integrated with Java language.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.105-110
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• 2014

Feature-oriented software product line engineering is to develop various products by developing product line core assets in terms of features and composing those features. However, the developed product may not behave correctly if the feature interaction problem has not be properly taken into account during the feature composition. This paper proposes techniques for identifying and modularizing undesirable feature interactions effectively. The scientific calculator product line is used for evaluating the applicability of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.373-375
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• 2002

빠르게 변화하는 시장의 요구에 대응하고자 특정 도메인에 속하는 애플리케이션 간의 재사용을 높이려는 Product-Line에 대한 연구가 진행되고 있다. Product-Line에서는 도메인 내의 여러 애플리케이션 간의 차이점과 공통정을 분류하는데 Feature Modeling이라는 개념을 주로 사용하고 있다. 이러한 Feature를 추상화하여 메타 모델로 나타내고 정령과 기법을 통하여 명세화 한다면, 기존의 Feature 모델에서 발견해내지 못하는 feature들과 관련 요소들을 추출하고, 명세화를 통한 통일된 해석이 가능하며 , Feature Model에 대한 reasoning, 충돌에 대한 예측 및 면상 등의 지원이 가능할 것이다. 따라서 본 논문에서는 Multi-paradigm 명세 언어를 제공하여주는 KAOS 방법을 적용하여, 추상화 수준에서의 메타 모델을 제안하고, Feature를 명세와 하고 습득하는 방안을 제시하고자 만다.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.93-104
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• 2009

Modular implementation of a feature is a first step toward feature-oriented product line engineering, which develops and then utilizes core assets to configure products in terms of features. Aspect-oriented programming provides effective mechanisms for improving the modularity of feature implementations. However, as features in general are not independent of each other, changes in the implementation of one feature may cause changes to or side effects in the implementation of other features. Moreover, since the time at which a feature is incorporated into products, called feature binding time, may be various from compile time through load time to run time, a feature may have to be implemented differently depending on when the feature is bound into a product. To make each feature implementation module as independent as possible, this paper proposes aspectual implementation patterns that can effectively separate feature dependencies as well as feature binding time from feature implementation modules. These patterns enable flexible composition of feature implementation modules without affecting other modules according to feature selection. The approaches are demonstrated and evaluated based on a product line of scientific calculator applications.

• Korea Trade Review
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• v.46 no.5
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• pp.99-113
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• 2021

This study aims to analyze the effect of product information quality and source credibility on product arousal in fresh food website. Despite fresh food websites are selling products with various feature, prior studies have focused on consumer behavior for fresh food website characteristics or specific products without considering the feature of the products. Consumers' attitudes, beliefs, and behaviors vary depending on the feature of the product. In other words, depending on the category of product, the decision making process that consumers purchase products can be differ. So, we classify products considering the feature of these products to examine the effect of information quality and source credibility on product arousal into experience goods and search goods. We surveyed 288 consumers having experience of purchase in fresh food website and verified the hypothesis through One-way ANOVA by classifying the information quality and the source credibility as high level and low level. As a result, there was a difference in product arousal according to the product information quality level and the source credibility level for each product category exposed to the fresh food website. In experience goods, source credibility have a more important effect on product arousal than product information quality, and in search goods, product information quality have a more important effect on product arousal than source credibility.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.803-814
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• 2008

Product line architectures include variable parts to be selected according to product specific requirements. In order to derive architectures that are valid for a particular product from product line architectures, variabilities of product line architectures must be systematically managed. In this paper, we adopt an approach to variability management of product line architectures through an explicit mapping between a feature model and product line architecture models. If this mapping is incorrect or there exists inconsistency among product line architectural elements, variabilities of product line architectures cannot be managed correctly. Therefore, this paper formally defines product line architectural models in terms of conceptual, process, deployment, and module views, and mapping relationships between the feature model and the architectural models. Consistency rules for correct variability management of product line architectures are defined in terms of consistency in each of product line architecture model, consistency between different architectural view models, and consistency between a feature model and product line architectural models. These consistency rules provide a theoretical foundation for deriving valid product architecture from product line architectures.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.1-6
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• 2014

Many companies have tried to adopt Software Product Line Engineering for improving the quality and productivity of information systems and software product. There are several models defined in software product line methodology and each model has different abstraction level. Therefor it is important to maintain the traceability and consistency between models. In this paper, consistency checking rules are suggested by traceability matrix of work products.