Persona: Fernández Amoros, David José
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Fernández Amoros
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David José
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Publicación Supporting the Statistical Analysis of Variability Models(Institute of Electrical and Electronics Engineers (IEEE), 2019-08-26) Mayr Dorn, Christoph; Egyed, Alexander; Heradio Gil, Rubén; Fernández Amoros, David JoséVariability models are broadly used to specify the configurable features of highly customizable software. In practice, they can be large, defining thousands of features with their dependencies and conflicts. In such cases, visualization techniques and automated analysis support are crucial for understanding the models. This paper contributes to this line of research by presenting a novel, probabilistic foundation for statistical reasoning about variability models. Our approach not only provides a new way to visualize, describe and interpret variability models, but it also supports the improvement of additional state-of-the-art methods for software product lines; for instance, providing exact computations where only approximations were available before, and increasing the sensitivity of existing analysis operations for variability models. We demonstrate the benefits of our approach using real case studies with up to 17,365 features, and written in two different languages (KConfig and feature models).Publicación Exemplar driven development of software product lines(Elsevier, 2012-12-01) Heradio Gil, Rubén; Fernández Amoros, David José; Torre Cubillo, Luis de la; Abad Cardiel, IsmaelThe benefits of following a product line approach to develop similar software systems are well documented. Nevertheless, some case studies have revealed significant barriers to adopt such approach. In order to minimize the paradigm shift between conventional software engineering and software product line engineering, this paper presents a new development process where the products of a domain are made by analogy to an existing product. Furthermore, this paper discusses the capabilities and limitations of different techniques to implement the analogy relation and proposes a new language to overcome such limitations.Publicación Pragmatic Random Sampling of the Linux Kernel: Enhancing the Randomness and Correctness of the conf Tool(Association for Computing Machinery, New York, 2024-09-02) Fernández Amoros, David José; Heradio Gil, Rubén; Horcas Aguilera, Jose Miguel; Galindo, José A.; Benavides, David; Fuentes, Lidia; https://orcid.org/0000-0003-3758-0195; https://orcid.org/0000-0002-5677-7156; https://orcid.org/0000-0002-8449-3273; https://orcid.org/0000-0001-9293-9784The configuration space of some systems is so large that it cannot be computed. This is the case with the Linux Kernel, which provides almost 19,000 configurable options described across more than 1,600 files in the Kconfig language. As a result, many analyses of the Kernel rely on sampling its configuration space (e.g., debugging compilation errors, predicting configuration performance, finding the configuration that optimizes specific performance metrics, etc.). The Kernel can be sampled pragmatically, with its built-in tool conf, or idealistically, translating the Kconfig files into logic formulas. The pros of the idealistic approach are that it provides statistical guarantees for the sampled configurations, but the cons are that it sets out many challenging problems that have not been solved yet, such as scalability issues. This paper introduces a new version of conf called randconfig+, which incorporates a series of improvements that increase the randomness and correctness of pragmatic sampling and also help validate the Boolean translation required for the idealistic approach. randconfig+ has been tested on 20,000 configurations generated for 10 different Kernel versions from 2003 to the present day. The experimental results show that randconfig+ is compatible with all tested Kernel versions, guarantees the correctness of the generated configurations, and increases conf’s randomness for numeric and string options.Publicación Anotación semántica no supervisada(Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos, 2004-11-29) Fernández Amoros, David José; Gonzalo Arroyo, Julio AntonioEn esta tesis se trata el problema de la desambiguación del sentido de las palabras (i.e. dados un diccionario, una palabra y un contexto, decidir en qué sentido del diccionario se está usando la palabra en el contexto). Las diferentes fuentes de información utilizadas son : 1. La información de origen taxonómico basada en la relación es-un, por ejemplo, un águila es-un pájaro. 2. La información de coocurrencias. Tomando como punto de partida un corpus de casi 300 millones de palabras provinientes de libros en formato electrónico (Proyecto Gutenberg) estudiaremos pares de palabras cuyas apariciones en contextos cortos son estadísticamente dependientes. Utilizaremos varias medidas para calibrar ese grado de dependencia y emplearemos dicha información para desambiguar. 3. Información extraída de la WWW. La información de la glosas del inventario de sentidos serán complementadas con información extraída de la Web. Esta información ha sido extraída de un sistema de clasificación de documentos realizado por voluntarios (Open Directory Project) por Celina Santamaría. 4. Información proviniente de corpora bilingüe comparable. Partiendo de un corpus en inglés y otro en español se han buscado patrones sintácticos superficiales correspondientes a sintagmas nominales en ambos idiomas. A partir de este trabajo realizado por Anselmo Peñas y Fernando López Ostenero estudiaremos si es posible aprovechar las diferencias entre ambos idiomas para detectar estos sintagmas y desambiguar mediante las capacidades translingües de una base de conocimiento léxica (EuroWordNet). Se demostrará que la anotación semántica no supervisada puede lograr buenos resultados, y que hay lineas de investigación, con un importante potencial de mejora, que merecen exploradas.Publicación Group Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis(MDPI, 2020) Heradio Gil, Rubén; Fernández Amoros, David José; Cobo, Manuel J.; Cerrada Collado, Cristina; https://orcid.org/0000-0002-7131-0482; https://orcid.org/0000-0001-6575-803XDecisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.Publicación Speeding up derivative configuration from product platforms(MDPI, 2014-06-18) Pérez Morago, Héctor José; Adán Oliver, Antonio; Heradio Gil, Rubén; Fernández Amoros, David JoséTo compete in the global marketplace, manufacturers try to differentiate their products by focusing on individual customer needs. Fulfilling this goal requires that companies shift from mass production to mass customization. Under this approach, a generic architecture, named product platform, is designed to support the derivation of customized products through a configuration process that determines which components the product comprises. When a customer configures a derivative, typically not every combination of available components is valid. To guarantee that all dependencies and incompatibilities among the derivative constituent components are satisfied, automated configurators are used. Flexible product platforms provide a big number of interrelated components, and so, the configuration of all, but trivial, derivatives involves considerable effort to select which components the derivative should include. Our approach alleviates that effort by speeding up the derivative configuration using a heuristic based on the information theory concept of entropy.Publicación Using Extended Logical Primitives for Efficient BDD Building(MDPI, 2020) Fernández Amoros, David José; Bra Gutiérrez, Sergio; Aranda Escolástico, Ernesto; Heradio Gil, RubénBinary Decision Diagrams (BDDs) have been used to represent logic models in a variety of research contexts, such as software product lines, circuit testing, and plasma confinement, among others. Although BDDs have proven to be very useful, the main problem with this technique is that synthesizing BDDs can be a frustratingly slow or even unsuccessful process, due to its heuristic nature. We present an extension of propositional logic to tackle one recurring phenomenon in logic modeling, namely groups of variables related by an exclusive-or relationship, and also consider two other extensions: one in which at least n variables in a group are true and another one for in which at most n variables are true. We add XOR, atLeast-n and atMost-n primitives to logic formulas in order to reduce the size of the input and also present algorithms to efficiently incorporate these constructions into the building of BDDs. We prove, among other results, that the number of nodes created during the process for XOR groups is reduced from quadratic to linear for the affected clauses. the XOR primitive is tested against eight logical models, two from industry and six from Kconfig-based open-source projects. Results range from no negative effects in models without XOR relations to performance gains well into two orders of magnitude on models with an abundance of this kind of relationship.Publicación Uniform and scalable sampling of highly configurable systems(Springer, 2022-01-21) Galindo, José A.; Benavides, David; Batory, Don; Heradio Gil, Rubén; Fernández Amoros, David José; Heradio Gil, Rubén; Fernández Amoros, David JoséMany analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These analyses could instead use statistical inference, where a tractable sample accurately predicts results for the entire space. To do so, the laws of statistical inference requires each member of the population to be equally likely to be included in the sample, i.e., the sampling process needs to be “uniform”. SAT-samplers have been developed to generate uniform random samples at a reasonable computational cost. However, there is a lack of experimental validation over colossal spaces to show whether the samplers indeed produce uniform samples or not. This paper (i) proposes a new sampler named BDDSampler, (ii) presents a new statistical test to verify sampler uniformity, and (iii) reports the evaluation of BDDSampler and five other state-of-the-art samplers: KUS, QuickSampler, Smarch, Spur, and Unigen2. Our experimental results show only BDDSampler satisfies both scalability and uniformity.Publicación Circuit Testing Based on Fuzzy Sampling with BDD Bases(University of Hawaiʻi at Mānoa, 2023) Pinilla, Elena; Fernández Amoros, David José; Heradio Gil, RubénFuzzy testing of integrated circuits is an established technique. Current approaches generate an approximately uniform random sample from a translation of the circuit to Boolean logic. These approaches have serious scalability issues, which become more pressing with the ever-increasing size of circuits. We propose using a base of binary decision diagrams to sample the translations as a soft computing approach. Uniformity is guaranteed by design and scalability is greatly improved. We test our approach against five other state-of-the-art tools and find our tool to outperform all of them, both in terms of performance and scalability.Publicación A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples(2022) Heradio Gil, Rubén; Fernández Amoros, David José; Ruiz Parrado, Victoria; Cobo, Manuel J.; https://orcid.org/0000-0003-2993-7705; http://orcid.org/ 0000-0001-6575-803XSoftware systems tend to become more and more configurable to satisfy the demands of their increasingly varied customers. Exhaustively testing the correctness of highly configurable software is infeasible in most cases because the space of possible configurations is typically colossal. This paper proposes addressing this challenge by (i) working with a representative sample of the configurations, i.e., a ``uniform'' random sample, and (ii) processing the results of testing the sample with a rule induction system that extracts the faults that cause the tests to fail. The paper (i) gives a concrete implementation of the approach, (ii) compares the performance of the rule learning algorithms AQ, CN2, LEM2, PART, and RIPPER, and (iii) provides empirical evidence supporting our procedure