Persona: Heradio Gil, Rubén
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Heradio Gil
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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 Finding Near-optimal Configurations in Colossal Spaces with Statistical Guarantees(Association for Computing Machinery (ACM), 2023-11-23) Oh, Jeho; Batory, Don; Heradio Gil, RubénA Software Product Line (SPL) is a family of similar programs. Each program is defined by a unique set of features, called a configuration, that satisfies all feature constraints. “What configuration achieves the best performance for a given workload?” is the SPLOptimization (SPLO) challenge. SPLO is daunting: just 80 unconstrained features yield 1024 unique configurations, which equals the estimated number of stars in the universe. We explain (a) how uniform random sampling and random search algorithms solve SPLO more efficiently and accurately than current machine-learned performance models and (b) how to compute statistical guarantees on the quality of a returned configuration; i.e., it is within x% of optimal with y% confidence.Publicación A bibliometric analysis of 10 years of research on symptom networks in psychopathology and mental health(Elsevier, 2022-02) Ausín, Berta; Castellanos, Miguel Ángel; González Sanguino, Clara; Heradio Gil, RubénPsychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). This article aims to analyze the research literature on network analysis in psychopathology and mental health for the last ten years. Statistical descriptive analysis was complemented with two bibliometric techniques: performance analysis and co-word analysis. There is an increase in publications that has passed from 1 article published in 2010 to 172 papers published in 2020. The 398 articles in the sample have 1,910 authors in total, being most of them occasional contributors. The Journal of Affective Disorders is the one with the highest number of publications on network analysis in psychopathology and mental health, followed by the Journal of Abnormal Psychology and Psychological Medicine stand out. The present study shows that this perspective in psychopathology and mental health is a recent field of study, but with solid advances in recent years from a wide variety of researchers, mainly from USA and Europe, who have extensively studied symptom networks in depression, anxiety, and post-traumatic stress disorders. However, gaps are identified in other psychological behaviors such as suicide, populations such as the elderly, and gender studies.Publicación A scalable approach to exact model and commonality counting for extended feature models.(Institute of Electrical and Electronics Engineers (IEEE), 2014-05-29) Fernández Amoros, David José; Heradio Gil, Rubén; Cerrada Somolinos, José Antonio; Cerrada Somolinos, CarlosA software product line is an engineering approach to efficient development of software product portfolios. Key to the success of the approach is to identify the common and variable features of the products and the interdependencies between them, which are usually modeled using feature models. Implicitly, such models also include valuable information that can be used by economic models to estimate the payoffs of a product line. Unfortunately, as product lines grow, analyzing large feature models manually becomes impracticable. This paper proposes an algorithm to compute the total number of products that a feature model represents and, for each feature, the number of products that implement it. The inference of both parameters is helpful to describe the standarization/parameterization balance of a product line, detect scope flaws, assess the product line incremental development, and improve the accuracy of economic models. The paper reports experimental evidence that our algorithm has better runtime performance than existing alternative approaches.Publicación A Monte Carlo tree search conceptual framework for feature model analyses(Elsevier, 2023-01) Horcas Aguilera, Jose Miguel ; Galindo, José A.; Benavides, David; Heradio Gil, Rubén; Fernández Amoros, David JoséChallenging domains of the future such as Smart Cities, Cloud Computing, or Industry 4.0 expose highly variable systems with colossal configuration spaces. The automated analysis of those systems’ variability has often relied on SAT solving and constraint programming. However, many of the analyses have to deal with the uncertainty introduced by the fact that undertaking an exhaustive exploration of the whole configuration space is usually intractable. In addition, not all analyses need to deal with the configuration space of the feature models, but with different search spaces where analyses are performed over the structure of the feature models, the constraints, or the implementation artifacts, instead of configurations. This paper proposes a conceptual framework that tackles various of those analyses using Monte Carlo tree search methods, which have proven to succeed in vast search spaces (e.g., game theory, scheduling tasks, security, program synthesis, etc.). Our general framework is formally described, and its flexibility to cope with a diversity of analysis problems is discussed. We provide a Python implementation of the framework that shows the feasibility of our proposal, identifying up to 11 lessons learned, and open challenges about the usage of the Monte Carlo methods in the software product line context. With this contribution, we envision that different problems can be addressed using Monte Carlo simulations and that our framework can be used to advance the state-of-the-art one step forward.Publicación Pragmatic random sampling of Kconfig-based systems: A unified approach(Elsevier, 2025-07-28) Fernández Amoros, David José; Heradio Gil, Rubén; Horcas Aguilera, Jose Miguel; Galindo, José A.; Benavides, David; Fuentes, LidiaThe configuration space of some systems is so large that it cannot be computed. This is the case with the Linux Kernel, which provides more than 18,000 configurable options described across almost 1,700 files in the Kconfig language. As a result, many analyses of these systems rely on sampling their configuration space (e.g., debugging compilation errors, predicting configuration performance, finding the configuration that optimizes specific performance metrics, among others.). The Kernel and other Kconfig-based systems can be sampled pragmatically, using their built-in tool conf to get a sample directly from the Kconfig specification that is approximately random, or idealistically, generating a genuine random sample by first translating the Kconfig files into logic formulas, then using a logic engine to compute the probability that each option value has to appear in a configuration, and finally utilizing these probabilities to generate an authentically random sample. The pros of the idealistic approach are that it ensures the sample is representative of the population, but the cons are that it sets out many challenging problems that have not been solved yet (fundamentally, how to obtain a valid translation into Boolean that covers all the Kconfig language, and how to compute the option value probabilities for very large formulas). 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 ten versions of the Linux Kernel and twenty additional Kconfig systems. Its compatibility significantly enhances the current landscape, where some systems use a customized conf variant that is maintained independently, while others do not support sampling at all. randconfig+ not only offers universal sampling for all Kconfig systems but also simplifies its evolutive maintenance as a single tool rather than an unorganized collection of conf variants.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 A bibliometric analysis of off-line handwritten document analysis literature (1990–2020)(Elsevier, 2022-05) Ruiz Parrado, Victoria; Vélez, José F.; Heradio Gil, Rubén; Aranda Escolástico, Ernesto; Sánchez Ávila, ÁngelProviding computers with the ability to process handwriting is both important and challenging, since many difficulties (e.g., different writing styles, alphabets, languages, etc.) need to be overcome for addressing a variety of problems (text recognition, signature verification, writer identification, word spotting, etc.). This paper reviews the growing literature on off-line handwritten document analysis over the last thirty years. A sample of 5389 articles is examined using bibliometric techniques. Using bibliometric techniques, this paper identifies (i) the most influential articles in the area, (ii) the most productive authors and their collaboration networks, (iii) the countries and institutions that have led research on the topic, (iv) the journals and conferences that have published most papers, and (v) the most relevant research topics (and their related tasks and methodologies) and their evolution over the years.Publicación Scalable Sampling of Highly-Configurable Systems: Generating Random Instances of the Linux Kernel(Association for Computing Machinery (ACM), 2023-01-05) Mayr Dorn, Christoph; Egyed, Alexander; Fernández Amoros, David José; Heradio Gil, RubénSoftware systems are becoming increasingly configurable. A paradigmatic example is the Linux kernel, which can be adjusted for a tremendous variety of hardware devices, from mobile phones to supercomputers, thanks to the thousands of configurable features it supports. In principle, many relevant problems on configurable systems, such as completing a partial configuration to get the system instance that consumes the least energy or optimizes any other quality attribute, could be solved through exhaustive analysis of all configurations. However, configuration spaces are typically colossal and cannot be entirely computed in practice. Alternatively, configuration samples can be analyzed to approximate the answers. Generating those samples is not trivial since features usually have inter-dependencies that constrain the configuration space. Therefore, getting a single valid configuration by chance is extremely unlikely. As a result, advanced samplers are being proposed to generate random samples at a reasonable computational cost. However, to date, no sampler can deal with highly configurable complex systems, such as the Linux kernel. This paper proposes a new sampler that does scale for those systems, based on an original theoretical approach called extensible logic groups. The sampler is compared against five other approaches. Results show our tool to be the fastest and most scalable one.
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