Improved RBF neural network-based multi-label metamorphic relationship prediction method

Abstract

The invention discloses an improved RBF neural network-based multi-label metamorphic relationship prediction method. The improved RBF neural network-based multi-label metamorphic relationship prediction method includes the following steps that: a control flow graph (CFG) is created from function source codes; one group of features is extracted from the control flow graph (CFG), wherein two kinds of features are extracted from the CFG, wherein one kind of features is node-based features, the other kind of features is path-based features; feature values and labels form a training set; an improved RBF neural network, namely, a calculating algorithm-k-means for optimizing a RBF neural network basis function center at a hidden layer, is adopted to perform clustering, and AP clustering is adopted to automatically find k values, and therefore, the number of nodes of the hidden layer can be obtained; a Huffman tree is constructed to select an initial clustering center; label counting vectors C embodying information among a plurality of labels are constructed; folded multiplication is performed on the label counting vectors C and RBF neural network basis function centers which are obtained through adopting improved k mean value clustering, so that new basis function centers can be obtained; and an RBF neural network model can be constructed so as to perform metamorphic relationship prediction.

Description

technical field [0001] The present invention relates to a method for predicting multi-label metamorphic relationships based on an improved RBF neural network, in particular, a multi-label data set and an improved RBF neural network, that is, an algorithm for obtaining the basis function center of an optimized hidden layer RBF neural network— K-means clustering, use AP clustering to automatically find the k value to obtain the number of nodes in the hidden layer, and construct a Huffman tree to select the initial cluster center, then construct a label count vector C that reflects the information between multiple labels, and then use it with The linear superposition of the RBF neural network basis function center obtained after improving the k-means clustering, obtains the new basis function center, and establishes a multi-label RBF neural network model to predict the transformation relationship, which belongs to the field of software testing. Background technique [0002] Wit...

AI Technical Summary

Problems solved by technology

[0002] With the development of economy and society, more and more software has been developed, but the quality of software has attracted widespread attention. Software testing is an important and indispensable software quality assurance technology, which is used to discover and correct existing problems in software defects and errors, but in most cases the expected output of the program under test is difficult to determine

Metamorphosis testing technology tests the program by checking the relationship between multiple execution results of the program, which can effectively solve the above problems, but the current metamorphosis testing technology also has some deficiencies. The construction method of metamorphic relationship, the lack of effective original test case selection strategy, and the lack of automated testing tools, etc., the test efficiency is low and the cost is high

[0003] At present, the research results on the construction of metamorphic relationships include predicting metamorphic relationships using machine learning technology and machine learning methods using graph kernels. These two methods are single-label metamorphic relationship prediction methods and can only predict one possible metamorphic relationship at a time. Therefore, the present invention proposes a multi-label metamorphic relationship prediction method based on an improved RBF neural network and can predict multiple metamorphic relationships that an application program may satisfy at one time, which can effectively solve the current ubiquitous construction method and lack of practical metamorphic relationships. Improving the construction efficiency of metamorphic relations

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