Revisión del 21:48 11 may 2017

SBSE Track in JISBD 2017

General information

The Spanish Conference on Software Engineering and Databases (JISBD) is a research forum for researchers from Spain, Portugal and South America working on these fields. The Ibero-American community around these research areas finds in JISBD an annual meeting point where researchers can present, discuss and interchange their research works and ideas, and network. JISBD 2017 is a multi-conference where the III Track on Search Based Software Engineering (SBSE) will be held.

Location and date: Tenerife, July, 19-21

Aims and scope

Within the Software Engineering field, the appearance of synergies with other areas makes possible the discovery of new ways to solve traditionally complex problems, such as project management, software testing, verification and validation, model driven software engineering, software design, requirements engineering, etc. In this context, the application of search and optimization techniques has lead to the so-called Search Based Software Engineering (SBSE). These automatic techniques provide the engineer with computational solutions that reduce the efforts and human costs required to their resolution.

Another type of algorithm techniques that can be applied to deal with Software Engineering problems is machine learning, a field within Artificial Intelligence that generates solutions based on learning from past experiences. It greatly improves the performance when solving specific problems like learning failure models, requirement extraction, cost prediction, etc. There are also interesting approaches in the context of decision support systems and data mining, whose study deserves further attention. Finally, another research area related with this track is Empirical Software Engineering, which is focused on drawing conclusions from experiments validated by statistical methods.

Temas de interés

Nuevos métodos de optimización aplicados a las distintas fases del ciclo de vida del software e integración y monitorización de sistemas.
Estudios empíricos y análisis teóricos sobre algoritmos de búsqueda para la ingeniería del software.
Aprendizaje automático aplicado a los nuevos tipos de sistemas software, entre los que se consideran las soluciones propuestas por la ingeniería dirigida por modelos, los sistemas autónomos, cloud computing, computación orientada a servicios, sistemas híbridos inteligentes, etc.
Aplicaciones del aprendizaje automático en proyectos software, incluyendo su especificación de requisitos, modelado, gestión y repositorios, etc.
Métodos de ingeniería del software empírica, incluyendo análisis estadísticos de datos empíricos y la validación de experimentos en el contexto de la ingeniería del software.
Nuevas propuestas para la minería de especificaciones software.
Herramientas SBSE y sistemas de recomendación y de apoyo a la decisión en la proceso de desarrollo del software.
Aplicaciones de técnicas de búsqueda a nuevos problemas de la ingeniería del software y disciplinas afines (arquitecturas de computación en la nube, sistemas dinámicos orientados a servicios, etc.).
Aplicaciones industriales y experiencias de SBSE.
Discusión de ideas emergentes sobre problemas relacionados con la temática.

Revisores

Enrique Alba, Universidad de Málaga
Isabel del Águila, Universidad de Almería
José del Sagrado, Universidad de Almería
Carmelo del Valle, Universidad de Sevilla
Antonia Estero-Botaro, Universidad de Cádiz
Javier Ferrer, Universidad de Málaga
Inmaculada Medina-Bulo, Universidad de Cádiz
Francisco Palomo-Lozano, Universidad de Cádiz
José Antonio Parejo, Universidad de Sevilla
Daniel Rodr´guez, Universidad de Alcalá
Sergio Segura, Universidad de Sevilla
Javier Tuya, Universidad de Oviedo
Sebastián Ventura, Universidad de Córdoba
Tanja Vos, Universidad Politécnica de Valencia

Coordinadores

José Rául Romero (Universidad de Córdoba)
Francisco Chicano (Universidad de Málaga)

@@ Línea 11: / Línea 11: @@
 === Aims and scope ===
-En la ingenier&iacute;a del software est&aacute;n apareciendo sinergias con otras &aacute;reas que están ayudando a descubrir nuevas formas de resolver problemas tradicionalmente complejos, como la gesti&oacute;n de proyectos, las pruebas del software, la verificaci&oacute;n y la validaci&oacute;n, la ingenier&iacute;a del software dirigida por modelos, el dise&ntilde;o, la ingenier&iacute;a de requisitos, etc. Una de estas sinergias es la consideraci&oacute;n de t&eacute;cnicas de optimizaci&oacute;n y b&uacute;squeda, que dan lugar a la ingenier&iacute;a del software basada en b&uacute;squeda (SBSE, ''Search-based Software Engineering''). Estas t&eacute;cnicas autom&aacute;ticas ofrecen al ingeniero soluciones computacionales que reducen el esfuerzo y coste de recursos humanos requeridos para su resoluci&oacute;n.
+Within the Software Engineering field, the appearance of synergies with other areas makes possible the discovery of new ways to solve traditionally complex problems, such as project management, software testing, verification and validation, model driven software engineering, software design, requirements engineering, etc. In this context, the application of search and optimization techniques has lead to the so-called Search Based Software Engineering (SBSE). These automatic techniques provide the engineer with computational solutions that reduce the efforts and human costs required to their resolution.
 Another type of algorithm techniques that can be applied to deal with Software Engineering problems is machine learning, a field within Artificial Intelligence that generates solutions based on learning from past experiences. It greatly improves the performance when solving specific problems like learning failure models, requirement extraction, cost prediction, etc. There are also interesting approaches in the context of decision support systems and data mining, whose study deserves further attention. Finally, another research area related with this track is Empirical Software Engineering, which is focused on drawing conclusions from experiments validated by statistical methods.