Resultados de búsqueda para Machine Learning

As in many other research areas, the use of Deep Learning (DL) techniques is growing in software engineering. However, these techniques are not yet widespread in the Model-Driven Engineering (MDE) field. In this paper, we explore the use of DL to extract useful text embeddings out of software models. We propose a novel approach to embedding software models by means of transformer architectures trained on large datasets. Our approach combines intermediate representations and Language Models (LMs) to extract features from modelling artefacts in order to enable applications of interest, like intelligent model assistance, classification, transformation,completion and correction, among others. We show that the approach is potentially useful in MDE and may lead to useful results in the future.

Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.

Recientes estudios han reportado efectos indeseados y nocivos en modelos de machine learning (ML), en gran parte causados por problemas o limitaciones en los datasets usados para entrenarlos. Esta situación ha despertado el interés dentro de la comunidad de ML para mejorar los procesos de creación y compartición de datasets. Sin embargo, hasta la fecha, las propuestas para estandarizar la descripción y formalización de los mismos se basan en guías generales en texto natural y que, como tales, presentan limitaciones (precisión, ambig+APw-edad, etc.) y son difíciles de aplicar de una forma (semi)automatizada.En este trabajo proponemos un lenguaje específico de dominio para describir datasets basado en las propuestas mencionadas. Este lenguaje contribuye a estandarizar los procesos de descripción de los datasets, y pretende ser la base para aplicaciones de formalización, búsqueda y comparación de estos. Finalmente, presentamos la implementación de este lenguaje en forma de plug-in para Visual Studio Code.

Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided.

Context:Leveraging machine learning techniques to address feature location on models has been gaining attention. Machine learning techniques empower software product companies to take advantage of the knowledge and the experience to improve the performance of the feature location process. Most of the machine learning-based works for feature location on models report the machine learning techniques and the tuning parameters in detail. However, these works focus on the size and the distribution of the data sets, neglecting the properties of their contents.Objective:In this paper, we analyze the influence of three model fragment properties (density, multiplicity, and dispersion) on a machine learning-based approach for feature location.Method:The analysis of these properties is based on an industrial case provided by CAF, a worldwide provider of railway solutions. The test cases were evaluated through a machine learning technique that uses different subsets of a knowledge base to learn how to locate unknown features.Results:Results show that the density and dispersion properties have a direct impact on the results. In our case study, the model fragments with extra-small density values achieve results with up to 43+ACU more precision, 41+ACU more recall, 42+ACU more F-measure, and 0.53 more Matthews Correlation Coefficient (MCC) than the model fragments with other density values. On the other hand, the model fragments with extra-small and small dispersion values achieve results with up to 53+ACU more precision, 52+ACU more recall, 52+ACU more F-measure, and 0.57 more MCC than the model fragments with other dispersion values.Conclusions:The analysis of the results shows that both density and dispersion properties significantly influence the results. These results can serve not only to improve the reports by means of the model fragment properties, but also to be able to compare machine learning-based feature location approaches fairly improving the feature location results.

Este es un artículo relevante ya publicado en la revista IEEE Access en el que se presenta un estudio exploratorio con 2 casos de análisis de un conjunto total de 87 aplicaciones Java que siguen el patrón Model-View-Controller (MVC) caracterizadas por 24 métricas de calidad y 2 tecnológicas. El estudio determina la similitud de estas aplicaciones en términos de calidad software mediante la adopción de el modelo de red neuronal no supervisada llamado Self-Organizing Maps (SOM). Además el artículo realiza una caracterización de las aplicaciones Java MVC y formaliza el proceso de cómo se ha realizado el estudio exploratorio mediante SPEM para que pueda ser reutilizado en otros trabajos de investigación.

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.