Handling class imbalance problem in software maintainability prediction: an empirical investigation
Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of softwar...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Malhotra, Ruchika [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Higher Education Press 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Frontiers of computer science in China - Beijing : Higher Education Press, 2007, 16(2021), 4 vom: 03. Dez. |
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| Übergeordnetes Werk: |
volume:16 ; year:2021 ; number:4 ; day:03 ; month:12 |
| Links: |
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| DOI / URN: |
10.1007/s11704-021-0127-0 |
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10.1007/s11704-021-0127-0 doi (DE-627)SPR053123182 (SPR)s11704-021-0127-0-e DE-627 ger DE-627 rakwb eng Malhotra, Ruchika verfasserin aut Handling class imbalance problem in software maintainability prediction: an empirical investigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. software maintenance (dpeaa)DE-He213 software maintainability (dpeaa)DE-He213 imbalanced learning (dpeaa)DE-He213 Lata, Kusum aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 16(2021), 4 vom: 03. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:16 year:2021 number:4 day:03 month:12 https://dx.doi.org/10.1007/s11704-021-0127-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 16 2021 4 03 12 |
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10.1007/s11704-021-0127-0 doi (DE-627)SPR053123182 (SPR)s11704-021-0127-0-e DE-627 ger DE-627 rakwb eng Malhotra, Ruchika verfasserin aut Handling class imbalance problem in software maintainability prediction: an empirical investigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. software maintenance (dpeaa)DE-He213 software maintainability (dpeaa)DE-He213 imbalanced learning (dpeaa)DE-He213 Lata, Kusum aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 16(2021), 4 vom: 03. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:16 year:2021 number:4 day:03 month:12 https://dx.doi.org/10.1007/s11704-021-0127-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 16 2021 4 03 12 |
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10.1007/s11704-021-0127-0 doi (DE-627)SPR053123182 (SPR)s11704-021-0127-0-e DE-627 ger DE-627 rakwb eng Malhotra, Ruchika verfasserin aut Handling class imbalance problem in software maintainability prediction: an empirical investigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. software maintenance (dpeaa)DE-He213 software maintainability (dpeaa)DE-He213 imbalanced learning (dpeaa)DE-He213 Lata, Kusum aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 16(2021), 4 vom: 03. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:16 year:2021 number:4 day:03 month:12 https://dx.doi.org/10.1007/s11704-021-0127-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 16 2021 4 03 12 |
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10.1007/s11704-021-0127-0 doi (DE-627)SPR053123182 (SPR)s11704-021-0127-0-e DE-627 ger DE-627 rakwb eng Malhotra, Ruchika verfasserin aut Handling class imbalance problem in software maintainability prediction: an empirical investigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. software maintenance (dpeaa)DE-He213 software maintainability (dpeaa)DE-He213 imbalanced learning (dpeaa)DE-He213 Lata, Kusum aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 16(2021), 4 vom: 03. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:16 year:2021 number:4 day:03 month:12 https://dx.doi.org/10.1007/s11704-021-0127-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 16 2021 4 03 12 |
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10.1007/s11704-021-0127-0 doi (DE-627)SPR053123182 (SPR)s11704-021-0127-0-e DE-627 ger DE-627 rakwb eng Malhotra, Ruchika verfasserin aut Handling class imbalance problem in software maintainability prediction: an empirical investigation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Higher Education Press 2022 Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. software maintenance (dpeaa)DE-He213 software maintainability (dpeaa)DE-He213 imbalanced learning (dpeaa)DE-He213 Lata, Kusum aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 16(2021), 4 vom: 03. Dez. (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:16 year:2021 number:4 day:03 month:12 https://dx.doi.org/10.1007/s11704-021-0127-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 AR 16 2021 4 03 12 |
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Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. © Higher Education Press 2022 |
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Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. © Higher Education Press 2022 |
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Abstract As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP. © Higher Education Press 2022 |
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