DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique
Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we pr...
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| Autor*in: |
Pandey, Sushant Kumar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 5(2023), 1 vom: 20. Nov. |
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| Übergeordnetes Werk: |
volume:5 ; year:2023 ; number:1 ; day:20 ; month:11 |
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| DOI / URN: |
10.1007/s42979-023-02364-1 |
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| Katalog-ID: |
SPR053806123 |
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| 520 | |a Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract | ||
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10.1007/s42979-023-02364-1 doi (DE-627)SPR053806123 (SPR)s42979-023-02364-1-e DE-627 ger DE-627 rakwb eng Pandey, Sushant Kumar verfasserin (orcid)0000-0003-1882-2435 aut DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract Software fault number prediction (dpeaa)DE-He213 Cross project (dpeaa)DE-He213 Defect count prediction (dpeaa)DE-He213 Restricted Boltzmann machines (dpeaa)DE-He213 Deep belief network (dpeaa)DE-He213 Long–short-term memory (dpeaa)DE-He213 Tripathi, Anil Kumar aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2023), 1 vom: 20. Nov. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2023 number:1 day:20 month:11 https://dx.doi.org/10.1007/s42979-023-02364-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2023 1 20 11 |
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10.1007/s42979-023-02364-1 doi (DE-627)SPR053806123 (SPR)s42979-023-02364-1-e DE-627 ger DE-627 rakwb eng Pandey, Sushant Kumar verfasserin (orcid)0000-0003-1882-2435 aut DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract Software fault number prediction (dpeaa)DE-He213 Cross project (dpeaa)DE-He213 Defect count prediction (dpeaa)DE-He213 Restricted Boltzmann machines (dpeaa)DE-He213 Deep belief network (dpeaa)DE-He213 Long–short-term memory (dpeaa)DE-He213 Tripathi, Anil Kumar aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2023), 1 vom: 20. Nov. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2023 number:1 day:20 month:11 https://dx.doi.org/10.1007/s42979-023-02364-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2023 1 20 11 |
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10.1007/s42979-023-02364-1 doi (DE-627)SPR053806123 (SPR)s42979-023-02364-1-e DE-627 ger DE-627 rakwb eng Pandey, Sushant Kumar verfasserin (orcid)0000-0003-1882-2435 aut DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract Software fault number prediction (dpeaa)DE-He213 Cross project (dpeaa)DE-He213 Defect count prediction (dpeaa)DE-He213 Restricted Boltzmann machines (dpeaa)DE-He213 Deep belief network (dpeaa)DE-He213 Long–short-term memory (dpeaa)DE-He213 Tripathi, Anil Kumar aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2023), 1 vom: 20. Nov. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2023 number:1 day:20 month:11 https://dx.doi.org/10.1007/s42979-023-02364-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2023 1 20 11 |
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10.1007/s42979-023-02364-1 doi (DE-627)SPR053806123 (SPR)s42979-023-02364-1-e DE-627 ger DE-627 rakwb eng Pandey, Sushant Kumar verfasserin (orcid)0000-0003-1882-2435 aut DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract Software fault number prediction (dpeaa)DE-He213 Cross project (dpeaa)DE-He213 Defect count prediction (dpeaa)DE-He213 Restricted Boltzmann machines (dpeaa)DE-He213 Deep belief network (dpeaa)DE-He213 Long–short-term memory (dpeaa)DE-He213 Tripathi, Anil Kumar aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2023), 1 vom: 20. Nov. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2023 number:1 day:20 month:11 https://dx.doi.org/10.1007/s42979-023-02364-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2023 1 20 11 |
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10.1007/s42979-023-02364-1 doi (DE-627)SPR053806123 (SPR)s42979-023-02364-1-e DE-627 ger DE-627 rakwb eng Pandey, Sushant Kumar verfasserin (orcid)0000-0003-1882-2435 aut DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract Software fault number prediction (dpeaa)DE-He213 Cross project (dpeaa)DE-He213 Defect count prediction (dpeaa)DE-He213 Restricted Boltzmann machines (dpeaa)DE-He213 Deep belief network (dpeaa)DE-He213 Long–short-term memory (dpeaa)DE-He213 Tripathi, Anil Kumar aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 5(2023), 1 vom: 20. Nov. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2023 number:1 day:20 month:11 https://dx.doi.org/10.1007/s42979-023-02364-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2023 1 20 11 |
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We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. 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Pandey, Sushant Kumar |
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Pandey, Sushant Kumar misc Software fault number prediction misc Cross project misc Defect count prediction misc Restricted Boltzmann machines misc Deep belief network misc Long–short-term memory DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique |
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DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique Software fault number prediction (dpeaa)DE-He213 Cross project (dpeaa)DE-He213 Defect count prediction (dpeaa)DE-He213 Restricted Boltzmann machines (dpeaa)DE-He213 Deep belief network (dpeaa)DE-He213 Long–short-term memory (dpeaa)DE-He213 |
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dbdnn-estimator: a cross-project number of fault estimation technique |
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DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique |
| abstract |
Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation (CPFNE) is one step ahead of CPFP, because it not only predicts faulty modules but also estimates the number of faults in that module. In this article, we proposed a new computational architecture using a deep belief network and deep neural network called DBDNN-Estimator for CPFNE. We investigated the effectiveness of our proposed approach on five projects and their respective versions from the PROMISE repository in our experiment and compared its performance over the existing eight benchmark approaches. We found that the proposed model required a few instances from the source project for optimal performance. Out of 23, we found that DBDNN-Estimator significantly outperforms in 19 and 14 data sets over baseline approaches in terms of mean absolute error (MAE) and mean squared error (MSE), respectively. The mean MAE and MSE produced by the proposed work are %$0.38\pm 0.023%$ and %$2.29\pm 0.18%$, respectively, which is minimum amongst benchmark techniques. We also found the Kendall and Fault Percentage Average (FPA) of the proposed model significantly better than baseline methods in 17 projects. We found the DBDNN-Estimator produces optimal results for small, moderate, and large-size software projects. The model is stable and tackles class imbalance and overfitting problems. Graphical Abstract © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique |
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https://dx.doi.org/10.1007/s42979-023-02364-1 |
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Tripathi, Anil Kumar |
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10.1007/s42979-023-02364-1 |
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2024-07-03T22:09:50.536Z |
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| score |
7.400199 |