Search-Based Web Service Antipatterns Detection

Service Oriented Architecture (SOA) is widely used in industry and is regarded as one of the preferred architectural design technologies. As with any other software system, service-based systems (SBSs) may suffer from poor design, i.e., antipatterns, for many reasons such as poorly planned changes, time pressure or bad design choices. Consequently, this may lead to an SBS product that is difficult to evolve and that exhibits poor quality of service (QoS). Detecting web service antipatterns is a manual, time-consuming and error-prone process for software developers. In this paper, we propose an automated approach for detection of web service antipatterns using a cooperative parallel evolutionary algorithm (P-EA). The idea is that several detection methods are combined and executed in parallel during an optimization process to find a consensus regarding the identification of web service anti patterns. We report the results of an empirical study using eight types of common web service antipatterns. We compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and one state-of-the-art detection technique not based on heuristic search. Statistical analysis of the obtained results demonstrates that our approach is efficient in antipattern detection, with a precision score of 89 percent and a recall score of 93 percent.