RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

Blog Article

Metamorphic testing has been successfully used in many different fields to solve the test oracle problem.However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task.Recently some Dog-Harnesses machine learning approaches have been proposed to predict metamorphic relations.These approaches predicting single label metamorphic relation can alleviate this problem to some extent.

However, many applications involve multi-group metamorphic relations, and these approaches are clearly inefficient.To address this problem, in this paper we propose a Multi-Label Metamorphic Relations prediction approach based on an improved radial basis function (RBF) neural network named RBF-MLMR.First, RBF-MLMR uses state-of-the-art soot analysis tool to Slim LED Downlight generate control flow graph and corresponds labels from the source codes of programs.Second, the extracted nodes and the path properties constitute multi-label data sets for the control flow graph.

Finally, a multi-label RBF neural network prediction model is established to predict whether the program satisfies multiple metamorphic relations.In order to improve the prediction results, affinity propagation and k-means clustering algorithms are used to optimize the RBF neural network structure of RBF-MLMR.A set of dedicated experiments based on public programs is conducted to validate RBF-MLMR.The experimental results show that RBF-MLMR can achieve accuracy of around 80% for predicting two and three metamorphic relations.