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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. | ||
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* New optimization methods applied to the different phases of the software life cycle, as well as integration and monitoring of systems. | * New optimization methods applied to the different phases of the software life cycle, as well as integration and monitoring of systems. | ||
Revisión del 17:28 20 feb 2018
SBSE Track in JISBD 2018
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 Software Engineering and databases. 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. Over the course of more than 20 editions, JISBD has significantly contributed to consolidate the iberoamerican research communities working on Software Engineering and databases. In the XXIII edition, JISBD is organised into tracks, among which the track on Search Based Software Engineering (SBSE) will be held.
Location and date: Sevilla, September, 17-19.
Chairs:
- José Raúl Romero Salguero (University of Córdoba)
- Inmaculada Medina Bulo (University of Cádiz)
Call for papers
The motivation behind this track comes from the appearance of synergies with other areas that are contributing to 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.
Topics of interest are (not limited to):
- 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, test case and data generation, 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.
Important dates
- Paper submission: March, 4
- Notification of acceptance: April, 22
- Camera ready: May, 13
- Conference: September, 17-19