An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors
<italic>Software Defects</italic> Prediction represents an essential activity during software development that contributes to continuously improving <italic>software quality</italic> and software maintenance and evolution by detecting defect-prone modules in new versions of a...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9793667/ |
| _version_ | 1811239053224312832 |
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| author | Diana-Lucia Miholca Vlad-Ioan Tomescu Gabriela Czibula |
| author_facet | Diana-Lucia Miholca Vlad-Ioan Tomescu Gabriela Czibula |
| author_sort | Diana-Lucia Miholca |
| collection | DOAJ |
| description | <italic>Software Defects</italic> Prediction represents an essential activity during software development that contributes to continuously improving <italic>software quality</italic> and software maintenance and evolution by detecting defect-prone modules in new versions of a software system. In this paper, we are conducting an in-depth analysis on the software features’ impact on the performance of deep learning-based software defect predictors. We further extend a large-scale feature set proposed in the literature for detecting defect-proneness, by adding conceptual software features that capture the semantics of the source code, including comments. The conceptual features are automatically engineered using Doc2Vec, an artificial neural network based prediction model. A broad evaluation performed on the Calcite software system highlights a statistically significant improvement obtained by applying deep learning-based classifiers for detecting software defects when using conceptual features extracted from the source code for characterizing the software entities. |
| first_indexed | 2024-04-12T12:53:02Z |
| format | Article |
| id | doaj.art-cdd01ca0f48f4f5e8ef60d5543ce902b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T12:53:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cdd01ca0f48f4f5e8ef60d5543ce902b2022-12-22T03:32:24ZengIEEEIEEE Access2169-35362022-01-0110648016481810.1109/ACCESS.2022.31819959793667An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect PredictorsDiana-Lucia Miholca0https://orcid.org/0000-0002-3832-7848Vlad-Ioan Tomescu1Gabriela Czibula2Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, RomaniaDepartment of Computer Science, Babes-Bolyai University, Cluj-Napoca, RomaniaDepartment of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania<italic>Software Defects</italic> Prediction represents an essential activity during software development that contributes to continuously improving <italic>software quality</italic> and software maintenance and evolution by detecting defect-prone modules in new versions of a software system. In this paper, we are conducting an in-depth analysis on the software features’ impact on the performance of deep learning-based software defect predictors. We further extend a large-scale feature set proposed in the literature for detecting defect-proneness, by adding conceptual software features that capture the semantics of the source code, including comments. The conceptual features are automatically engineered using Doc2Vec, an artificial neural network based prediction model. A broad evaluation performed on the Calcite software system highlights a statistically significant improvement obtained by applying deep learning-based classifiers for detecting software defects when using conceptual features extracted from the source code for characterizing the software entities.https://ieeexplore.ieee.org/document/9793667/Deep learningDoc2veclatent semantic indexingsoftware defect prediction |
| spellingShingle | Diana-Lucia Miholca Vlad-Ioan Tomescu Gabriela Czibula An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors IEEE Access Deep learning Doc2vec latent semantic indexing software defect prediction |
| title | An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors |
| title_full | An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors |
| title_fullStr | An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors |
| title_full_unstemmed | An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors |
| title_short | An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors |
| title_sort | in depth analysis of the software features x2019 impact on the performance of deep learning based software defect predictors |
| topic | Deep learning Doc2vec latent semantic indexing software defect prediction |
| url | https://ieeexplore.ieee.org/document/9793667/ |
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