Prediction of Web Service Antipatterns Using Machine Learning

Abstract

Web service interfaces are considered as one of the critical components of a Service-Oriented Architecture (SOA) and they represent contracts between web service providers and clients (subscribers). These interfaces are frequently modified to meet new requirements. However, these changes in a web service interface typically affect the systems of its subscribers. Thus, it is important for subscribers to estimate the risk of using a specific service and to compare its evolution to other services offering the same features in order to reduce the effort of adapting their applications in the next releases. In addition, the prediction of interface changes may help web service providers to better manage available resources (e.g. programmers’ availability, hard deadlines, etc.) and efficiently schedule required maintenance activities to improve the quality. In this research, we propose to use machine learning, based on times series, for the prediction of web service antipatterns. To this end, we collected training data from quality metrics of previous releases from 8 web services. The validation of our prediction techniques shows that the predicted metrics value, such as number of operations, which are used to feed the antipattern detection rules on the different releases of the 8 web services were similar to the expected ones with a very low deviation rate. In addition, most of the quality issues of the studied Web service interfaces were accurately predicted, for the next releases. The survey conducted with active developers also shows the relevance of prediction technique for both service providers and subscribers.