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Many IT enterprises today use Service Oriented Architecture(SOA) as the effective architectural approach for building their systems. Service-Based Systems(SBS) like other complex frameworks are liable to change to fit in the new user requirements. These may lead to the deterioration of the quality and design of the software systems and may cause the materialization of poor solutions called Anti-patterns. Similar to object-oriented systems, web services also suffer from anti-patterns due to bad programming practices, design, and implementation. An anti-pattern is defined as a commonly used process, structure, or pattern of action that, despite initially appearing to be an effective and appropriate response to a problem, has more bad consequences than good ones. Anti- pattern detection using Web Service Description Language(WSDL) metrics can be used as a part of the software development life cycle to reduce the maintenance of the software system and also to improve the quality of the software. The work is motivated by the need to develop an automatic predictive model for the prediction of web services anti- patterns using static analysis of the WSDL metrics. The core ideology of this work is to empirically investigate the effectiveness of classifier techniques i.e, ensemble and deep learning techniques in the prediction of web service anti-patterns. In this paper, we present an empirical analysis on the application of seven feature selection techniques, six data sampling techniques, and ten classifier techniques for the prediction of four different types of anti-patterns. The results confirm the predictive ability of WSDL metrics in the prediction of SOA anti-patterns. |
