Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction
Software systems exist in different critical domains. Software reliability assessment has become a critical issue due to the variety levels of software complexity. Software reliability, as a sub-branch of software quality, has been exploited to evaluate to what extend the desired software is trustab...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sulaimani Polytechnic University
2017-08-01
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| Series: | Kurdistan Journal of Applied Research |
| Subjects: | |
| Online Access: | http://kjar.spu.edu.iq/index.php/kjar/article/view/70 |
| _version_ | 1819080417323515904 |
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| author | Shirin Noekhah Naomie binti Salim Nor Hawaniah Zakaria |
| author_facet | Shirin Noekhah Naomie binti Salim Nor Hawaniah Zakaria |
| author_sort | Shirin Noekhah |
| collection | DOAJ |
| description | Software systems exist in different critical domains. Software reliability assessment has become a critical issue due to the variety levels of software complexity. Software reliability, as a sub-branch of software quality, has been exploited to evaluate to what extend the desired software is trustable. To overcome the problem of dependency to human power and time limitation for software reliability prediction, researchers consider soft computing approaches such as Neural Network and Fuzzy Logic. These techniques suffer from some limitations including lack of analyzing mathematical foundations, local minima trapping and convergence problem. This study develops a novel model for software reliability prediction through the combination of Multi-Layer Perceptron Neural Network (MLP) and Imperialist Competitive Algorithm (ICA). The proposed model has solved some of the problems of existing methods such as convergence problem and demanding on huge number of data. This model can be used in complicated software systems. The results prove that both training and testing phases of this model outperform existing approaches in terms of predicting the number of software failures. |
| first_indexed | 2024-12-21T19:44:33Z |
| format | Article |
| id | doaj.art-c32a443b5419463385ca9f582e7a2099 |
| institution | Directory Open Access Journal |
| issn | 2411-7684 2411-7706 |
| language | English |
| last_indexed | 2024-12-21T19:44:33Z |
| publishDate | 2017-08-01 |
| publisher | Sulaimani Polytechnic University |
| record_format | Article |
| series | Kurdistan Journal of Applied Research |
| spelling | doaj.art-c32a443b5419463385ca9f582e7a20992022-12-21T18:52:22ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062017-08-012315216010.24017/science.2017.3.570Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability PredictionShirin Noekhah0Naomie binti Salim1Nor Hawaniah Zakaria2Faculty of Computing, Universiti Teknologi of Malaysia, UTM, 81300, Johor, MalaysiaFaculty of Computing, Universiti Teknologi of Malaysia, UTM, 81300, Johor, MalaysiaFaculty of Computing, Universiti Teknologi of Malaysia, UTM, 81300, Johor, MalaysiaSoftware systems exist in different critical domains. Software reliability assessment has become a critical issue due to the variety levels of software complexity. Software reliability, as a sub-branch of software quality, has been exploited to evaluate to what extend the desired software is trustable. To overcome the problem of dependency to human power and time limitation for software reliability prediction, researchers consider soft computing approaches such as Neural Network and Fuzzy Logic. These techniques suffer from some limitations including lack of analyzing mathematical foundations, local minima trapping and convergence problem. This study develops a novel model for software reliability prediction through the combination of Multi-Layer Perceptron Neural Network (MLP) and Imperialist Competitive Algorithm (ICA). The proposed model has solved some of the problems of existing methods such as convergence problem and demanding on huge number of data. This model can be used in complicated software systems. The results prove that both training and testing phases of this model outperform existing approaches in terms of predicting the number of software failures.http://kjar.spu.edu.iq/index.php/kjar/article/view/70Soft computing, reliability of software, Multi-Layer Perceptron Neural Network, Imperialist Competitive Algorithm |
| spellingShingle | Shirin Noekhah Naomie binti Salim Nor Hawaniah Zakaria Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction Kurdistan Journal of Applied Research Soft computing, reliability of software, Multi-Layer Perceptron Neural Network, Imperialist Competitive Algorithm |
| title | Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction |
| title_full | Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction |
| title_fullStr | Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction |
| title_full_unstemmed | Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction |
| title_short | Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction |
| title_sort | applying neural network approach with imperialist competitive algorithm for software reliability prediction |
| topic | Soft computing, reliability of software, Multi-Layer Perceptron Neural Network, Imperialist Competitive Algorithm |
| url | http://kjar.spu.edu.iq/index.php/kjar/article/view/70 |
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