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* Francisco Chicano (University of Málaga) | * Francisco Chicano (University of Málaga) | ||
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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. | ||
| − | === | + | {| class="wikitable" |
| + | !<center>'''Artículo'''</center> | ||
| + | !<center>'''Enlaces'''</center> | ||
| + | |- | ||
| + | |J. A. Parejo, A. B. Sánchez, S. Segura, A. Ruiz-Cortés, R. E. Lopez-Herrejón, A. Egyed. ''Multi-Objective Test Case Prioritization in Highly Configurable Systems: A Case Study'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://doi.org/10.1016/j.jss.2016.09.045]] | ||
| + | [[Image:Icono_presentacion.png|25px|link=http://www.uco.es/investigacion/proyectos/SEBASENet/sebasenet-docs/jisbd17/slides/jisbd17-parejo.pdf]] | ||
| + | |- | ||
| + | |P. Delgado-Pérez, I. Medina-Bulo. ''Evaluación de la Mejora de Conjuntos de Casos de Prueba mediante la Prueba de Mutación Evolutiva'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/evaluacion-de-la-mejora-de-conjuntos-de-casos-de-prueba-mediante-la-prueba-de-mutacion-evolutiva/]] | ||
| + | |- | ||
| + | |P. Delgado-Pérez, I. Medina-Bulo. ''Mejora del Rendimiento de la Prueba de Mutación Evolutiva mediante la Reducción de Mutantes Equivalentes'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/mejora-del-rendimiento-de-la-prueba-de-mutacion-evolutiva-mediante-la-reduccion-de-mutantes-equivalentes/]] | ||
| + | |- | ||
| + | |R. Morales, Z. Soh, F. Khomh, G. Antoniol, F. Chicano. ''On the use of developers context for automatic refactoring of software anti-patterns'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://doi.org/10.1016/j.jss.2016.05.042]] | ||
| + | |- | ||
| + | |A. Arrieta, S. Wang, G. Sagardui, L. Etxeberria. ''Test Case Prioritization of Configurable Cyber-Physical Systems with Weight-Based Search Algorithms'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://doi.org/10.1145/2908812.2908871]] | ||
| + | |- | ||
| + | |J. Del Sagrado, I. M. Del Águila, A. J. Bosch, F. Chicano. ''Impacto de las métricas CK en la refactorización'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/impacto-de-las-metricas-ck-en-la-refactorizacion/]] | ||
| + | |- | ||
| + | |A. Ramírez, J.R. Romero, S. Ventura. ''Búsqueda coevolutiva interactiva aplicada al diseño de software'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/busqueda-coevolutiva-interactiva-aplicada-al-diseno-de-software/]][[Image:Icono_presentacion.png|25px|link=http://www.uco.es/investigacion/proyectos/SEBASENet/sebasenet-docs/jisbd17/slides/jisbd17-ramirez.pdf]] | ||
| + | |- | ||
| + | |M. Martínez, J. C. Riquelme, R. Ruiz, D. Rodríguez. ''Predicción de módulos defectuosos como un problema de optimización multiobjetivo'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/prediccion-de-modulos-defectuosos-como-un-problema-de-optimizacion-multiobjetivo/]] | ||
| + | |- | ||
| + | |M. Á. Domínguez, F. Chicano, E. Alba. ''Hacia un Algoritmo Exacto para Resolver el Problema de Agrupamiento de Módulos Software'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/hacia-un-algoritmo-exacto-para-resolver-el-problema-de-agrupamiento-de-modulos-software/]] | ||
| + | |- | ||
| + | |R. Barbudo, A. Ramírez, J.R. Romero, S. Ventura. ''Descubrimiento de patrones de diseño basado en buenas prácticas: modelo y discusión'' | ||
| + | |[[Image:Icono_textoweb.png|25px|link=https://biblioteca.sistedes.es/articulo/descubrimiento-de-patrones-de-diseno-basado-en-buenas-practicas-modelo-y-discusion/]] | ||
| + | |- | ||
| + | |R. Hierons, M. Li, X. Liu, S. Segura, W. Zheng. ''SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization'' ('''''Salón de la Fama''''') | ||
| + | ||[[Image:Icono_textoweb.png|25px|link=https://doi.org/10.1145/2897760]] | ||
| + | |- | ||
| + | |} | ||
Revisión del 21:18 28 jul 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
Chairs:
- José Rául Romero (University of Córdoba)
- Francisco Chicano (University of Málaga)
Accepted papers
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.
| J. A. Parejo, A. B. Sánchez, S. Segura, A. Ruiz-Cortés, R. E. Lopez-Herrejón, A. Egyed. Multi-Objective Test Case Prioritization in Highly Configurable Systems: A Case Study | 
|
| P. Delgado-Pérez, I. Medina-Bulo. Evaluación de la Mejora de Conjuntos de Casos de Prueba mediante la Prueba de Mutación Evolutiva |
|
| P. Delgado-Pérez, I. Medina-Bulo. Mejora del Rendimiento de la Prueba de Mutación Evolutiva mediante la Reducción de Mutantes Equivalentes |
|
| R. Morales, Z. Soh, F. Khomh, G. Antoniol, F. Chicano. On the use of developers context for automatic refactoring of software anti-patterns |
|
| A. Arrieta, S. Wang, G. Sagardui, L. Etxeberria. Test Case Prioritization of Configurable Cyber-Physical Systems with Weight-Based Search Algorithms |
|
| J. Del Sagrado, I. M. Del Águila, A. J. Bosch, F. Chicano. Impacto de las métricas CK en la refactorización |
|
| A. Ramírez, J.R. Romero, S. Ventura. Búsqueda coevolutiva interactiva aplicada al diseño de software | 
|
| M. Martínez, J. C. Riquelme, R. Ruiz, D. Rodríguez. Predicción de módulos defectuosos como un problema de optimización multiobjetivo |
|
| M. Á. Domínguez, F. Chicano, E. Alba. Hacia un Algoritmo Exacto para Resolver el Problema de Agrupamiento de Módulos Software |
|
| R. Barbudo, A. Ramírez, J.R. Romero, S. Ventura. Descubrimiento de patrones de diseño basado en buenas prácticas: modelo y discusión |
|
| R. Hierons, M. Li, X. Liu, S. Segura, W. Zheng. SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization (Salón de la Fama) |
|