Visualized Feature Modeling in Software Product Line

2009 ◽  
pp. 299-310
Author(s):  
Li Zheng ◽  
Chao Zhang ◽  
Zhanwei Wu ◽  
Yixin Yan
2019 ◽  
Vol 12 (1) ◽  
pp. 59
Author(s):  
Ikram Dehmouch ◽  
Bouchra El Asri ◽  
Maryem Rhanoui ◽  
Mina El Maallam

Feature modeling is used to express commonality and variability among a family of software products called the software product line. To offer customized products to their customers, organizations need to build packages of features taking into consideration customer needs and preferences. This paper presents a platform named SPLP (Software Product Line Profiling) which allows pre-configuring feature models through the restriction of the configuration space to meet the requirements of a specific market segment. Considering that concerns and preferences of this latter are a key criteria to achieve a tailored pre-configuration, authors propose the integration of user profiling in the SPLP platform through the definition of a user profile model describing information about the user and the products he is used to consume. This information is then exploited by the SPLP platform to perform an automated pre-configuration according to each user profile requirements and preferences.


2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Elias Kuiter ◽  
Sebastian Krieter ◽  
Jacob Krüger ◽  
Gunter Saake ◽  
Thomas Leich

AbstractFeature models are a helpful means to document, manage, maintain, and configure the variability of a software system, and thus are a core artifact in software product-line engineering. Due to the various purposes of feature models, they can be a cross-cutting concern in an organization, integrating technical and business aspects. For this reason, various stakeholders (e.g., developers and consultants) may get involved into modeling the features of a software product line. Currently, collaboration in such a scenario can only be done with face-to-face meetings or by combining single-user feature-model editors with additional communication and version-control systems. While face-to-face meetings are often costly and impractical, using version-control systems can cause merge conflicts and inconsistency within a model, due to the different intentions of the involved stakeholders. Advanced tools that solve these problems by enabling collaborative, real-time feature modeling, analogous to Google Docs or Overleaf for text editing, are missing. In this article, we build on a previous paper and describe (1) the extended formal foundations of collaborative, real-time feature modeling, (2) our conflict resolution algorithm in more detail, (3) proofs that our formalization converges and preserves causality as well as user intentions, (4) the implementation of our prototype, and (5) the results of an empirical evaluation to assess the prototype’s usability. Our contributions provide the basis for advancing existing feature-modeling tools and practices to support collaborative feature modeling. The results of our evaluation show that our prototype is considered helpful and valuable by 17 users, also indicating potential for extending our tool and opportunities for new research directions.


Author(s):  
Hitesh Yadav ◽  
Rita Chhikara ◽  
Charan Kumari

Background: Software Product Line is the group of multiple software systems which share the similar set of features with multiple variants. Feature model is used to capture and organize features used in different multiple organization. Objective: The objective of this research article is to obtain an optimized subset of features which are capable of providing high performance. Methods: In order to achieve the desired objective, two methods have been proposed. a) An improved objective function which is used to compute the contribution of each feature with weight based methodology. b) A hybrid model is employed to optimize the Software Product Line problem. Results: Feature sets varying in size from 100 to 1000 have been used to compute the performance of the Software Product Line. Conclusion: The results shows that proposed hybrid model outperforms the state of art metaheuristic algorithms.


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