Optimized approach for antipattern detection in service computing architecture

Abstract

Maintainability of a software can be enhanced by using refactoring activities. Refactoring activities aims at eliminating antipatterns from the system Application of refactoring techniques negatively impacts the power and energy consumption behavior of software systems. In other words, maintainability sometimes leads to sustainability degradation. Antipatterns depict suboptimal implementation choices that lead to more error prone and highly maintainable systems. A large number of antipatterns have been defined in the literature which are used by developers to detect system design issues. Despite the large number of antipatterns which are defined, their accuracy is still a subjective measure. Objective: There are multiple issues like uncertainty, boundary definition, metric combination and rule conversion. To overcome these limitations the research paper focusses on the use of optimized algorithms for antipattern detection. Method: The research paper deals with the existing antipattern detection techniques and proposal of a new optimized algorithm which not only works on static metrics but also takes into consideration dynamic metrics for execution. Later on the results are optimized using genetic algorithm. A comparative analysis of the existing models with the proposed model is also done. The analysis clearly depicted that the proposed approach has better precision and recall rate of approximately 90 %. Result: The proposed approach was successful in identification of five antipatterns (Multi Service, Fine Grained Web Service, Chatty Web Service, Data Web Service and Ambiguous Web Service).