RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network
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 machine learning approaches have been proposed to pr...
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| Format: | Article |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8055540/ |
| _version_ | 1818619201152090112 |
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| author | Pengcheng Zhang Xuewu Zhou Patrizio Pelliccione Hareton Leung |
| author_facet | Pengcheng Zhang Xuewu Zhou Patrizio Pelliccione Hareton Leung |
| author_sort | Pengcheng Zhang |
| collection | DOAJ |
| description | 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 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 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. |
| first_indexed | 2024-12-16T17:33:43Z |
| format | Article |
| id | doaj.art-00a7e95b3402499dbc55e6927ed47d13 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:33:43Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
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| spelling | doaj.art-00a7e95b3402499dbc55e6927ed47d132022-12-21T22:22:52ZengIEEEIEEE Access2169-35362017-01-015217912180510.1109/ACCESS.2017.27587908055540RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural NetworkPengcheng Zhang0https://orcid.org/0000-0003-3594-408XXuewu Zhou1Patrizio Pelliccione2Hareton Leung3College of Computer and Information, Hohai University, Nanjing, ChinaCollege of Computer and Information, Hohai University, Nanjing, ChinaDepartment of Computer Science and Engineering, Chalmers University of Technology, Göteborg, SwedenDepartment of Computing, Hong Kong Polytechnic University, HongKongMetamorphic 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 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 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.https://ieeexplore.ieee.org/document/8055540/Multi-labelmetamorphic testingmetamorphic relationlabel count vectorRBF neural network |
| spellingShingle | Pengcheng Zhang Xuewu Zhou Patrizio Pelliccione Hareton Leung RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network IEEE Access Multi-label metamorphic testing metamorphic relation label count vector RBF neural network |
| title | RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network |
| title_full | RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network |
| title_fullStr | RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network |
| title_full_unstemmed | RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network |
| title_short | RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network |
| title_sort | rbf mlmr a multi label metamorphic relation prediction approach using rbf neural network |
| topic | Multi-label metamorphic testing metamorphic relation label count vector RBF neural network |
| url | https://ieeexplore.ieee.org/document/8055540/ |
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