Producción CyT
Lecture Notes in Computer Science (LNCS) - A Novel Unsupervised Learning Approach for Assessing Web Services Refactoring
Congreso
Autoría:
Guillermo Rodríguez ; Cristian Mateos ; Luciano Listorti ; Brian Hammer ; Sanjay MisraFecha:
2019Editorial y Lugar de Edición:
SpringerResumen *
During the last years, the development of Service-Orientedapplications has become a trend. Given the characteristics and challengesposed by current systems, it has become essential to adopt this solutionsince it provides a great performance in distributed and heterogeneousenvironments. At the same time, the necessity of flexibility and greatcapacity of adaptation introduce a process of constant modifications andgrowth. Thus, developers easily make mistakes such as code duplicationor unnecessary code, generating a negative impact on quality attributessuch as performance and maintainability. Refactoring is considered atechnique that greatly improves the quality of software and provides asolution to this issue. In this context, our work proposes an approachfor comparing manual service groupings and automatic groupings thatallows analyzing, evaluating and validating clustering techniques appliedto improve service cohesion and fragmentation. We used V-Measure withhomogeneity and completeness as the evaluation metrics. Additionally,we have performed improvements in existing clustering techniques of aprevious work, VizSOC, that reach 20% of gain regarding the aforementioned metrics. Moreover, we added an implementation of the COBWEBclustering algorithm yielding fruitful results. Información suministrada por el agente en SIGEVAPalabras Clave
UNSUPERVISED MACHINE LEARNINGSERVICE GROUPINGWEB SERVICESOFTWARE REFACTORING