| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Ingeniería de requisitos'''. NRP (''Next Release Problem'') es un destacado problema que consiste en seleccionar el conjunto óptimo de requisitos a desarrollar en la siguiente iteración de un proyecto, sujeto a diversas restricciones (Pitangueira ''et al.'', 2015). Uno de los desafíos es resolver eficazmente la versión multi-objetivo del problema, minimizando el coste de los requisitos a la vez que se maximiza el beneficio esperado (Del Sagrado ''et al''., 2015). Otros retos son la aplicación de algoritmos exactos y estudiar cómo influyen los posibles errores en la estimación de los requisitos (Harman ''et al''., 2014). El problema NRP puede extenderse para considerar varias versiones anticipadamente, incorporar la asignación de recursos, etc., lo que da lugar a problemas muy complejos pero de gran aplicabilidad práctica.</font>
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| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Requirements Engineering'''. The Next Release Problem (NRP) is a well-known SBSE problem that aims at selecting the optimal set of requirements to be implemented in the next project iteration, subject to diverse constraints (Pitanguera ''et al''., 2015). One challenge here is to address the multi-objective version of the problem, where the costs associated to the requirements should be minimized while maximizing the expected benefit (Del Sagrado ''et al''., 2015). Other challenges include the application of exact algorithms and the study of influence of estimation errors (Harman ''et al''., 2014). NRP can be also extended to consider incoming releases, to integrate resource allocation, etc., leading to complex scenarios but with great applicability in practice.</font>
| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Diseño automático de software'''. Las tareas de análisis y diseño del software están fuertemente vinculadas a decisiones humanas, por lo que el éxito en su realización recae en la experiencia y habilidades de los expertos. A pesar de las dificultades que plantea, SBSE también ha comenzado a abordar su resolución de manera automática (Räihä, 2010). Actualmente se están realizando importantes esfuerzos en tareas como la ingeniería inversa para líneas de producto (Lopez-Herrejon ''et al''., 2015), el diseño de servicios web (Parejo ''et al''., 2014) o la optimización de arquitecturas software (Ramírez ''et al''., 2015b). En este ámbito se hace necesario considerar la construcción de modelos metaheurísticos destinados a dar soporte al ingeniero, más que a sustituirlo, con el fin de apoyarle durante la concepción, modificación y mejora del software desde una fase temprana de su desarrollo.</font>
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| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Automatic software design'''. Tasks belonging to the analysis and design phases are clearly related to human decisions, so their success strongly depends on the engineer’s expertise and abilities. Despite the related difficulties, SBSE has started to face the automatic design of software in an automatic way (Räihä, 2010). Great efforts are made to address tasks like reverse engineering for software product lines (Lopez-Herrejon ''et al''., 2015), web services design (Parejo ''et al''., 2014) or software architecture optimization (Ramírez ''et al''., 2015b). In this scenario, metaheuristic models should be viewed as a support to software engineers, instead of their replacement. Therefore, the objective is to assist them during the conception, modification and enhancement of software since its very early development.</font>
| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Algoritmos interactivos (''human-in-the-loop'')'''. Existen tareas que son complejas de simular, y cuyas soluciones son difíciles de evaluar por la máquina. Por ejemplo, se podría pensar en el análisis de un sistema. Es por ello fundamental considerar la participación del ingeniero en el propio proceso de optimización, comúnmente llamado ''human-in-the-loop'', con el fin de incorporar sus habilidades y así lograr resultados más satisfactorios. Aunque se han realizado aportaciones en el ámbito del diseño software (Simons and Parmee, 2012; Simons ''et al''., 2014) y la generación de pruebas (Marculescu ''et al''., 2015), la interacción entre los algoritmos de búsqueda y los expertos aún requiere un estudio profundo, ya que entran en juego aspectos como el rol del experto en la búsqueda, las necesidades específicas del problema a resolver o la fatiga asociada al proceso (Ramírez ''et al''., 2015a).</font>
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| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Interactive algorithms (''human-in-the-loop'')'''. Software Engineering tasks can be complex to simulate, especially those when the automatic evaluation of the quality of solutions is not possible. The system analysis phase might be an example. In this scenario, the active participation of the expert in the optimization process is a must, implying the consideration of the ''human-in-the-loop''. This kind of approaches allows integrating expert's abilities with the search process in order to obtain more satisfactory results. Although there are some proposals in areas like software design (Simons and Parmee, 2012; Simons ''et al''., 2014) and testing (Marculescu ''et al''., 2015), interactive mechanisms still require an in-depth analysis. Factors usually involved in interactive approaches are the role of the expert in the search process, the specific requirements of the problem under study or the user fatigue (Ramírez ''et al''., 2015a).</font>
| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Pruebas software'''. ''Search Based Software Testing'' (SBST) constituye una de las ramas más estudiadas y fructíferas de SBSE (Domínguez-Jiménez ''et al''., 2011; Lopez-Herrejon ''et al''., 2014; Ferrer ''et al''., 2015). Recientemente se han identificado tres líneas de investigación prometedoras en SBST (Harman ''et al''., 2015): la automatización de pruebas no funcionales, con especial atención al consumo energético; la búsqueda de estrategias de prueba, en oposición a los casos de prueba; y la optimización de varios objetivos simultáneamente (optimización multi-objetivo), tales como la cobertura, el tiempo de ejecución, o la memoria requerida. Harman ''et al''. auguran un futuro prometedor a herramientas basadas en búsqueda que sean capaces de encontrar errores en el software, resolverlos y verificar las soluciones, todo automáticamente (''FiFiVerify tools'').</font>
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| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Software testing'''. Search Based Software Testing (SBST) represents one of the most studied and fruitful areas of SBSE (Domínguez-Jiménez ''et al.'', 2011; Lopez-Herrejon ''et al.'', 2014; Ferrer ''et al.'', 2015). Recently, three promising research lines have been identified (Harman ''et al''., 2015): the automation of non-functional test cases, putting especial attention into energy consumption; the search of testing strategies in constrast to the generation of test cases; or the joint optimization of several objectives (multi-objective optimization), such as test coverage, execution time or required memory. Harman ''et al''. already announced a promising future for those search-based tools capable of finding bugs, solving them and verifying solutions in a fully automatic way (''FiFiVerify tools'').</font>
| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Estimación de costes software'''. La estimación de costes es un aspecto de gran importancia durante el desarrollo del software que también puede abordarse desde la perspectiva de SBSE (Dolado, 2001). En este campo se han propuesto diversos modelos metaheurísticos, aunque su efectividad todavía no ha demostrado ser superior a los métodos clásicos. Varios participantes en esta Red han realizado evaluaciones de los modelos de estimación utilizando técnicas de análisis de equivalencia (Dolado et al, 2014). En este sentido, sería necesario disponer del mayor número posible de modelos para poder realizar una valoración exhaustiva. </font>
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| style="background-color:#ffffff; text-align:justify;" | <font size=3>'''Software cost estimation'''. Software cost estimation is a key aspect throughout the software life cycle that can be also addressed from a SBSE perspective (Dolado, 2001). Within this field, there are several proposals using metaheuristic models, though their effectiveness has not been demonstrated to be higher than classic methods. Some participants of the SEBASENet consortium have performed evaluations of estimation models using equivalence analysis techniques (Dolado ''et al''., 2014). It would be desirable to count with a greater number of models in order to complete a more comprehensive assessment.</font>
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Revisión del 12:33 12 jun 2016
Requirements Engineering. The Next Release Problem (NRP) is a well-known SBSE problem that aims at selecting the optimal set of requirements to be implemented in the next project iteration, subject to diverse constraints (Pitanguera et al., 2015). One challenge here is to address the multi-objective version of the problem, where the costs associated to the requirements should be minimized while maximizing the expected benefit (Del Sagrado et al., 2015). Other challenges include the application of exact algorithms and the study of influence of estimation errors (Harman et al., 2014). NRP can be also extended to consider incoming releases, to integrate resource allocation, etc., leading to complex scenarios but with great applicability in practice.
Automatic software design. Tasks belonging to the analysis and design phases are clearly related to human decisions, so their success strongly depends on the engineer’s expertise and abilities. Despite the related difficulties, SBSE has started to face the automatic design of software in an automatic way (Räihä, 2010). Great efforts are made to address tasks like reverse engineering for software product lines (Lopez-Herrejon et al., 2015), web services design (Parejo et al., 2014) or software architecture optimization (Ramírez et al., 2015b). In this scenario, metaheuristic models should be viewed as a support to software engineers, instead of their replacement. Therefore, the objective is to assist them during the conception, modification and enhancement of software since its very early development.
Interactive algorithms (human-in-the-loop). Software Engineering tasks can be complex to simulate, especially those when the automatic evaluation of the quality of solutions is not possible. The system analysis phase might be an example. In this scenario, the active participation of the expert in the optimization process is a must, implying the consideration of the human-in-the-loop. This kind of approaches allows integrating expert's abilities with the search process in order to obtain more satisfactory results. Although there are some proposals in areas like software design (Simons and Parmee, 2012; Simons et al., 2014) and testing (Marculescu et al., 2015), interactive mechanisms still require an in-depth analysis. Factors usually involved in interactive approaches are the role of the expert in the search process, the specific requirements of the problem under study or the user fatigue (Ramírez et al., 2015a).
Software testing. Search Based Software Testing (SBST) represents one of the most studied and fruitful areas of SBSE (Domínguez-Jiménez et al., 2011; Lopez-Herrejon et al., 2014; Ferrer et al., 2015). Recently, three promising research lines have been identified (Harman et al., 2015): the automation of non-functional test cases, putting especial attention into energy consumption; the search of testing strategies in constrast to the generation of test cases; or the joint optimization of several objectives (multi-objective optimization), such as test coverage, execution time or required memory. Harman et al. already announced a promising future for those search-based tools capable of finding bugs, solving them and verifying solutions in a fully automatic way (FiFiVerify tools).
Software cost estimation. Software cost estimation is a key aspect throughout the software life cycle that can be also addressed from a SBSE perspective (Dolado, 2001). Within this field, there are several proposals using metaheuristic models, though their effectiveness has not been demonstrated to be higher than classic methods. Some participants of the SEBASENet consortium have performed evaluations of estimation models using equivalence analysis techniques (Dolado et al., 2014). It would be desirable to count with a greater number of models in order to complete a more comprehensive assessment.
