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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.
 
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.
  
=== Topics of interest ===
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=== Temas de interés ===
  
 
The following list compiles the topics of interest for the track:
 
The following list compiles the topics of interest for the track:

Revisión del 20:47 11 may 2017


SBSE Track in JISBD 2017

Información general

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.

Lugar y fecha de celebración: Tenerife, del 19 al 21 de julio de 2017.

Aims and scope

En la ingeniería del software están apareciendo sinergias con otras áreas que están ayudando a descubrir nuevas formas de resolver problemas tradicionalmente complejos, como la gestión de proyectos, las pruebas del software, la verificación y la validación, la ingeniería del software dirigida por modelos, el diseño, la ingeniería de requisitos, etc. Una de estas sinergias es la consideración de técnicas de optimización y búsqueda, que dan lugar a la ingeniería del software basada en búsqueda (SBSE, Search-based Software Engineering). Estas técnicas automáticas ofrecen al ingeniero soluciones computacionales que reducen el esfuerzo y coste de recursos humanos requeridos para su resolución.

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

The following list compiles the topics of interest for the track:

  • New optimization methods applied to the different phases of the software life cycle, as well as integration and monitoring of systems.
  • Empirical studies and theoretical analyses on search algorithms applied to Software Engineering.
  • Machine Learning applied to novel software systems, including model driven engineering, autonomous systems, cloud computing, services oriented computing, hybrid artificial systems, etc.
  • Machine learning applications to software projects, including requirement specification, modeling, management and repositories, etc.
  • Empirical Software Engineering methods, including the statistical analysis of empirical data and the validation of experiments in the context of Software Engineering.
  • New proposals for mining software specifications.
  • SBSE tools, recommender systems, and decision support systems to assist engineers during the software development process.
  • Applications of search techniques to new problems in Software Engineering and other related areas (cloud computing architectures, dynamic service oriented systems, etc.)
  • Industrial applications and experiences in SBSE.
  • Discussion of emerging topics related to the field.

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

Chairs

  • José Rául Romero (University of Córdoba)
  • Francisco Chicano (University of Málaga)