Persona: Heradio Gil, Rubén
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Heradio Gil
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Rubén
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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 procedurePublicación 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.