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Daniel Rodriguez
Researcher at University of Alcalá
Publications - 98
Citations - 1661
Daniel Rodriguez is an academic researcher from University of Alcalá. The author has contributed to research in topics: Software & Feature selection. The author has an hindex of 23, co-authored 97 publications receiving 1472 citations. Previous affiliations of Daniel Rodriguez include University of Reading & Hospital General Universitario Gregorio Marañón.
Papers
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Journal ArticleDOI
Exploring affiliation network models as a collaborative filtering mechanism in e-learning
Daniel Rodriguez,Miguel-Angel Sicilia,Salvador Sánchez-Alonso,Leonardo Lezcano,Elena García-Barriocanal +4 more
TL;DR: The results of the case study show that techniques such as blockmodeling and m-slices can be used to filter participants for rearranging groups; rearrange topics of interest; and dynamically change the structure of a course.
Book ChapterDOI
Convertibility between IFPUG and COSMIC functional size measurements
TL;DR: This paper proposes a model to convert functional size measures obtained with the IFPUG method to the corresponding COSMIC measures and presents the validation of the model using 33 software projects measured with both methods.
Comparación de diferentes algoritmos de clustering en laestimación de coste en el desarrollo de software
TL;DR: In this work one considers, like improvement of the estimation process, to segment the ISBSG data base in different groups from projects by means of the use of three different clustering algorithms: COBWEB, EM, and k-means, so that for each one of these clusters (formed by homogenous projects to each other) a different mathematical relation is obtained.
Journal ArticleDOI
Using simulation-based optimization in the context of IT service management change process
TL;DR: The results of applying two well-known Multi-Objective Evolutionary Algorithms, namely NSGA-II and SPEA2, are shown to obtain a set of optimal solutions for the KPIs associated with delivering process efficiency as a CSF.
Proceedings ArticleDOI
Automatically Classifying Requirements from App Stores: A Preliminary Study
TL;DR: This paper applies self-labeling algorithms as Semi-Supervised Classification (SSC) techniques in order to automate the classification of functional and non-functional requirements contained in reviews in the App Store.