JosA� Antonio Parejo y Aurora RamA�rez

QoS-Aware web service composition with multi-objective A�evolutionary algorithms

Service-based applications often invoke web services provided by third parties in its workflow. The Quality of Service provided by service providers is usually expressed in terms of a Service Level Agreement, that specifies the cost, performance, availability, etc. In this scenario, intelligent systems can help the engineers to scrutinize the service market, in order to select those service configurations that best fit their needs.
This search problem, also known as a QoS-aware web services composition, needs to simultaneously take into account multiple quality attributes which may be in conflict. For instance, faster response time entails a higher cost. Therefore, several quality properties must be optimized simultaneously using multi-objective or many–objective approaches, which require computationally efficient algorithms.
This session presents the QoS-aware web services composition problem and its various variants, as well as a comparative experimental study of multi-objective and many-objective algorithms. Specifically, we explore the suitability of various evolutionary algorithms to address the problem on the basis of a set of real web services with 9 quality properties. It is observed that some algorithms can achieve a better balance between the quality properties, or even promote specific properties while maintaining high quality values a��a��for the rest. Furthermore, this search process can be performed within a reasonable computational cost, allowing its adoption by intelligent systems and enabling decision support in the field of service-oriented computing.

Daniel RodrA�guez

Software Defect Prediction in Software Engineering

In this talk, we present how data mining techniques are used to predict or rank error-prone modules in software engineering. Classifying or ranking software components according to their probability of being defective helps with the testing and maintenance phases of a project to, for example, allocate resources, prioritising modules to be tested or perform regression testing activities. We will review the publicly available datasets, machine learning techniques and some of the machine learning problems that we face during the process. On the one hand, from software engineering point of view, we need to deal with data quality and what software engineering metrics can used. On the other hand, form the data mining point of view, we may need to deal with feature selection and issues such noise and missing values, imbalanced data, and the evaluation measures of the machine learning algorithms and their comparison.