Resultados de investigación

Data from the article "F. Ortin, G. Facundo, M. Garcia. Analyzing syntactic constructs of Java programs with machine learning. Expert Systems with Applications (215), pp. 119398-119414, 2023. https://doi.org/10.1016/j.eswa.2022.119398", The massive number of open-source projects in public repositories has notably increased in the last years. Such repositories represent valuable information to be mined for different purposes, such as documenting recurrent syntactic constructs, analyzing the particular constructs used by experts and beginners, using them to teach programming and to detect bad programming practices, and building programming tools such as decompilers, Integrated Development Environments or Intelligent Tutoring Systems. An inherent problem of source code is that its syntactic information is represented with tree structures, while traditional machine learning algorithms use n-dimensional datasets. Therefore, we present a feature engineering process to translate tree structures into homogeneous and heterogeneous n-dimensional datasets to be mined. Then, we run different interpretable (supervised and unsupervised) machine learning algorithms to mine the syntactic information of more than 17 million syntactic constructs in Java code. The results reveal interesting information such as the Java constructs that are barely (and widely) used (e.g., bitwise operators, union types and static blocks), different language features and patterns mostly (and barely) used by beginners (and experts), the discovery of particular types of source code (e.g., helper or utility classes, data transfer objects and too complex abstractions), and how complexity is an inherent characteristic in some clusters of syntactic constructs., This work has been partially funded by the Spanish Department of Science, Innovation and Universities: project RTI2018-099235-B-I00. The authors have also received funds from the University of Oviedo, Spain through its support of official research groups (GR-2011-0040).

Data from the article "F. Ortin, M. Garcia, B. Garcia Perez-Schofield, J. Quiroga. The StaDyn Programming Language. SoftwareX (20), pp. 101211-101222, 2022. https://doi.org/10.1016/j.softx.2022.101211", Hybrid static and dynamic typing languages are aimed at combining the benefits of both kinds of languages: the early type error detection and compile-time optimizations of static typing, together with the runtime adaptability of dynamically typed languages. The StaDyn programming language is a hybrid typing language, whose main contribution is the utilization of the type information gathered by the compiler to improve compile-time error detection and runtime performance. StaDyn has been evaluated as the hybrid typing language for the .Net platform with the highest runtime performance and the lowest memory consumption. Although most optimizations are performed statically by the compiler, compilation time is yet lower than the existing hybrid languages implemented on the .Net platform., The StaDyn programming language was partially funded by Microsoft Research, United States with the project entitled Extending Dynamic Features of the SSCLI, awarded in the Phoenix and SSCLI, Compilation and Managed Execution Request for Proposals. It was also funded by the Spanish Department of Science, Innovation, and Universities (project RTI2018-099235-B-I00) and the University of Oviedo, Spain (GR-2011-0040).

Dataset asociado a la publicación Data for 20L Siwek CFD simulations, These data include code to compile in OpenFOAM, calculation mesh and configuration of the different cases used in the publication http://hdl.handle.net/10651/66758, Authors acknowledge that this work was partially funded by CDTI (Centro para el Desarrollo Tecnologico Industrial de España, IDI-20191151), Universidad de Oviedo and PHB WESERHUTTE, S.A., under the project "FUO-047-20: Desarrollo de silo metalico de grandes dimensiones ante los condicionantes de explosividad de la biomasa". A. Islas acknowledges support from the research grant #BP20-124 under the 2020 Severo Ochoa Pre (Doctoral) Program of the Principality of Asturias.

The authors wish to thank the financial support of the Spanish Ministry of Economy, Industry and Competitiveness for the R + D Project entitled “Development and Construction of Vertical Axis Wind Turbines for Urban Environments” (DEVTURB) – Ref. ENE2017-89965-P, under the National Plan for Scientific and Technical Research and Innovation. The grant provided by the Principality of Asturias for the support of Research Activities, funded by the Institute for Economic Development (IDEPA) under reference GRUPIN IDI/2018/000205 is also gratefully acknowledged. Additionally, the support given by the University Institute for Industrial Technology of Asturias (IUTA) and the City Hall of Gijón, through the financed project SV-18-GIJON-1-05, is also recognized.

Data collected for the controlled experiment performed to analyze whether gamification can help in software testing education., MCIN/AEI/10.13039/501100011033 (Spain)

Spanish Ministry of Economy and Competitiveness (MINECO), Spanish Ministry of Science and Innovation (MICINN) respectively and Asturias Regional Government.

El estudio “Valoración de la calidad de la FP dual por parte del alumnado” es un trabajo conjunto entre la Universidad Autónoma de Barcelona y la Universidad de Oviedo (grupo EFI), la Fundación Bertelsmann y la empresa Lidl Supermercados SAU, y se enmarca en el proyecto Somos FP dual. El estudio se centra en analizar la satisfacción de los/las estudiantes con la estancia de FP dual que han realizado en la empresa. Para ello, del alumnado responden un cuestionario al finalizar su estancia en la empresa, dando su opinión sobre diversos aspectos de la FP dual como son la vinculación con la empresa, el proceso de formación, el seguimiento y evaluación, la continuidad tras finalizar la estancia y la utilidad de la formación recibida, Lidl Supermercados SAU

Code and data of the article: Target inductive methods for zero-shot regression https://doi.org/10.1016/j.ins.2022.03.075

This dataset is directly associated with the article “Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times” (Swarm and Evolutionary Computation, 2022) and contains the complete set of benchmark instances and experimental results reported in the publication., This dataset contains benchmark problem instances and detailed experimental results associated with the SWEVO 2022 article on green job shop scheduling with uncertain times. It includes the solved instances used in the experimental evaluation and the corresponding numerical results, provided both in the original spreadsheet format and in interoperable CSV files (one per instance). The dataset has been archived to support transparency, reproducibility and reuse of the experimental evidence reported in the associated publication., This research has been supported by the Spanish Government under research grants TIN2016-79190-R and PID2019-106263RB-I00, and by the Principality of Asturias Government under grant IDI/2018/000176.