Efficient elicitation of software configurations using crowd preferences and domain knowledge

Gonzalez-Fernandez, Yasser; Hamidi, Saeideh; Chen, Stephen; Liaskos, Sotirios

doi:10.1007/s10515-018-0247-4

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…To improve efficiency, the algorithm applies the Eckart-Young theorem [33] that obtains an approximation with only k factors (k < n), so that the matrices would remain as U n×k , S k×k and V t k×m , as it is shown in Equation 7. SVD(M 1m×n ) U m×k × S k×k × V T k×n (7) After this process, calculating the prediction of the user u k on the item p is reduced by multiplying the k-th row vector of the matrix U (i.e., U (u k )) with the matrix S and the p − th vector column of the matrix V t , (V t (p)) as shown in the Equation 8.…”

Section: ) Collaborative-based Recommender Systemsmentioning

confidence: 99%

“…• Implemented Plugins Identification: Once the list of websites was obtained, the next step was to identify which Wordpress plugins are implemented to each one of them. For this process, we used the Wptheme detector tools 6 and Wordpress Plugin Checker 7 where we obtained a list of plugins for each site. In some cases, we checked the source code of the site to identify plugins that were not detected by the aforementioned tools.…”

Section: ) Wordpress Feature Modelmentioning

confidence: 99%

“…Comparing the works described above with RESDEC, based on the characteristics of columns [4][5][6][7] of table 9, we can say that one of the main advantages of our proposal is the use of a knowledge base that feeds of information that comes from the experience that ordinary users have had when using products in an SPL domain. Specifically, in our proposal this information is represented by the ratings that users have made about the components used to implement features in the configuration of a product.…”

Section: Related Workmentioning

confidence: 99%

“…These studies address configuration from different perspectives and contexts. For example, feature model structural properties [7]- [11] in which the use of recommendation techniques are used for the configurations selection and prioritization. Also, using quality attributes [12]- [15] which use contextual information of features to select optimal configurations and offer a more personalized product to the user.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress

et al. 2019

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In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature. In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected. In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings. In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration. We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration. The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results. Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.

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Section: ) Collaborative-based Recommender Systemsmentioning

confidence: 99%

Section: ) Wordpress Feature Modelmentioning

confidence: 99%