The impact of feature selection on maintainability prediction of service-oriented applications
Service-oriented development methodologies are very often considered for distributed system development. The quality of service-oriented computing can be best assessed by the use of software metrics that are considered to design the prediction model. Feature selection technique is a process of selec...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
SpringerLink
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/53802 |
| _version_ | 1848759231639977984 |
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| author | Kumar, L. Krishna, Aneesh Rath, S. |
| author_facet | Kumar, L. Krishna, Aneesh Rath, S. |
| author_sort | Kumar, L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Service-oriented development methodologies are very often considered for distributed system development. The quality of service-oriented computing can be best assessed by the use of software metrics that are considered to design the prediction model. Feature selection technique is a process of selecting a subset of features that may lead to build improved prediction models. Feature selection techniques can be broadly classified into two subclasses such as feature ranking and feature subset selection technique. In this study, eight different types of feature ranking and four different types of feature subset selection techniques have been considered for improving the performance of a prediction model focusing on maintainability criterion. The performance of these feature selection techniques is evaluated using support vector machine with different types of kernels over a case study, i.e., five different versions of eBay Web service. The performances are measured using accuracy and F-measure value. The results show that maintainability of the service-oriented computing paradigm can be predicted by using object-oriented metrics. The results also show that it is possible to find a small subset of object-oriented metrics which helps to predict maintainability with higher accuracy and also reduces the value of misclassification errors. |
| first_indexed | 2025-11-14T09:56:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-53802 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:56:36Z |
| publishDate | 2016 |
| publisher | SpringerLink |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-538022017-10-17T05:20:39Z The impact of feature selection on maintainability prediction of service-oriented applications Kumar, L. Krishna, Aneesh Rath, S. Service-oriented development methodologies are very often considered for distributed system development. The quality of service-oriented computing can be best assessed by the use of software metrics that are considered to design the prediction model. Feature selection technique is a process of selecting a subset of features that may lead to build improved prediction models. Feature selection techniques can be broadly classified into two subclasses such as feature ranking and feature subset selection technique. In this study, eight different types of feature ranking and four different types of feature subset selection techniques have been considered for improving the performance of a prediction model focusing on maintainability criterion. The performance of these feature selection techniques is evaluated using support vector machine with different types of kernels over a case study, i.e., five different versions of eBay Web service. The performances are measured using accuracy and F-measure value. The results show that maintainability of the service-oriented computing paradigm can be predicted by using object-oriented metrics. The results also show that it is possible to find a small subset of object-oriented metrics which helps to predict maintainability with higher accuracy and also reduces the value of misclassification errors. 2016 Journal Article http://hdl.handle.net/20.500.11937/53802 10.1007/s11761-016-0202-9 SpringerLink restricted |
| spellingShingle | Kumar, L. Krishna, Aneesh Rath, S. The impact of feature selection on maintainability prediction of service-oriented applications |
| title | The impact of feature selection on maintainability prediction of service-oriented applications |
| title_full | The impact of feature selection on maintainability prediction of service-oriented applications |
| title_fullStr | The impact of feature selection on maintainability prediction of service-oriented applications |
| title_full_unstemmed | The impact of feature selection on maintainability prediction of service-oriented applications |
| title_short | The impact of feature selection on maintainability prediction of service-oriented applications |
| title_sort | impact of feature selection on maintainability prediction of service-oriented applications |
| url | http://hdl.handle.net/20.500.11937/53802 |