An empirical analysis on webservice antipattern prediction in different variants of machine learning perspective

Anti-patterns are explicit structures in the design that represents a significant violation of software design principles and negatively impacts the software design quality. The presence of these Anti-patterns highly influences the maintainability and perception of software systems. Thus it becomes necessary to predict anti-patterns at the early stage and refactor them to improve the software quality in terms of execution cost, maintenance cost, and memory consumption. In the anti-pattern prediction domain, during research analysis, it was realized that there had been very little work instigated on addressing both class imbalance and feature redundancy problems jointly to enhance models' performance and prediction accuracy. It has been perceived in the literature survey to study droughts with a comprehensive comparative analysis of different sampling and feature selection strategies. To achieve greater precision results and performance, this research constructs a web service anti-pattern prediction model over preprocessed software source code metrics using sampling and feature selection techniques to handle imbalanced data and feature redundancy to gain flawless web service anti-pattern prediction outcomes. Considering the above erudition, we have applied different variants of aggregation measures to find the metrics at the system level. These extracted metrics are used as input, so we have also applied different variants of feature selection techniques to remove irrelevant features and select the best combination of features. After finding important features, we have also applied different variants of data sampling techniques to overcome the problem of class imbalance. Finally, we have used thirty-three different classifiers to find import patterns that help identify anti-patterns. These all techniques are compared using Accuracy and Area Under the ROC (receiver operating characteristic curve) Curve (AUC). The experimental result of web service anti-pattern prediction models validated on 226 WSDL files illustrates that the least square support vector machine (LSSVM) with RBF kernel attains the best performance among the other 33 competing classifiers employed with the lowest Friedman mean rank value of 1.18. During comparative analysis over different feature subset selection techniques, the outcome indicates the mean accuracy value of 88.40% and mean AUC value of 0.88 for the models developed using significant features are higher in comparison to other techniques. The result shows the up-sampling methods (UPSAM) method secured the highest mean accuracy % and mean AUC with values of 86.14% and 0.87, respectively. The experimental result indicates the performance of the web service anti-pattern prediction models is adversely impacted by class imbalance and irrelevance of features. The outcome demonstrates that the performance of trained models improved with an AUC value between 0.805 to 0.99 post-application of sampling and feature selection strategies without using feature selection and sampling techniques. The outcome implies that USMAP achieves better performance. The result demonstrates that the models developed using significant features drive the desired effect compared to other implemented feature selection techniques.