MLAPW: A framework to assess the impact of feature selection and sampling techniques on anti-pattern prediction using WSDL metrics

The quality and design of Service-Based Systems may be degraded because of frequent changes, and negatively impacts the software design quality called Anti-patterns . The existence of these Anti-patterns highly impacts the overall maintainability of Service-Based Systems. Hence, early detection of these anti-patterns’ presence becomes mandatory with co-located modifications. However, it is not easy to find these anti-patterns manually. The objective of this work is to explore the role of WSDL (Web Services Description Language) metrics (MLAPW) for anti-pattern prediction using a Machine Learning (ML) based framework. This framework encompasses different variants of feature selection techniques, data sampling techniques, and a wide range of ML algorithms. This work empirically investigates the predictive ability of anti-pattern prediction models developed using different sets of WSDL metrics. Our major focus is to investigate ’ how these metrics accurately predict different types of Anti-patterns present in the WSDL file ’. To achieve the objective, different sets of WSDL metrics such as Structural Quality Metrics, Procedural Quality Metrics, Data Quality Metrics, Quality Metrics, and Complexity metrics, are used as input for Anti-patterns prediction models. Since these models use WSDL metrics as input, we have also used feature selection methods to find the best sets of WSDL metrics. These models are trained using various machine-learning techniques. This study also shows the performance of these models trained on balanced data using data sampling techniques. Finally, the empirical investigation of these techniques was done using accuracy and ROC (receiver operating characteristic curve) curve (AUC) with hypothesis testing. The empirical study’s observation is based on 226 WSDL files from various domains such as finance, tourism, health, education, etc. The assessment asserts that the models trained using WSDL metrics have 0.79 mean AUC and 0.90 Median AUC. However, the models trained using the selected feature with classifier feature subset selection (CFS) have a better mean AUC of 0.80 and median AUC of 0.97. The experimental results also confirm that the models trained on up-sampling (UPSAM) have a better mean AUC of 0.79 and median AUC of 0.91 with a low value of Friedman rank of 2.40. Finally, the models trained using the least square support vector machine (LSSVM) achieved 1 median AUC, 0.99 mean AUC, and a low Friedman rank of 1.30. The experimental results show that the AUC values of the models trained using Data and Procedural Quality Metrics are high as compared to the other sets of metrics. However, the models improved significantly in their prediction performance after employing feature selection techniques. The experimental results also show that the models trained using the advanced level of classifiers and ensemble learning have a higher value of AUC than other techniques. Based on this research, it is reasonable to claim that using data sampling techniques helps to improve the models’ prediction capability. The models trained on sampled data using UPSAM or up-sampling achieved 0.91 medians AUC and 0.79 average AUC.