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* Francisco Chicano (University of Málaga) | * Francisco Chicano (University of Málaga) | ||
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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. | 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. | 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. | ||
Revisión del 18:37 28 oct 2016
SBSE Track in JISBD 2016
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. The XXI edition is co-located with the V Spanish Congress on Computer Science (CEDI). JISBD is a multi-conference where the II Track on Search Based Software Engineering (SBSE) will be held.
Location and date: Salamanca, September, 13-16.
Chairs:
- José Rául Romero (University of Córdoba)
- Francisco Chicano (University of Málaga)
Accepted papers
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.