Just-in-time defect prediction for mobile applications: using shallow or deep learning?
<p dir="ltr">Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have b...
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2023
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| _version_ | 1864513530575716352 |
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| author | Raymon van Dinter (10521952) |
| author2 | Cagatay Catal (6897842) Görkem Giray (5291287) Bedir Tekinerdogan (6897839) |
| author2_role | author author author |
| author_facet | Raymon van Dinter (10521952) Cagatay Catal (6897842) Görkem Giray (5291287) Bedir Tekinerdogan (6897839) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raymon van Dinter (10521952) Cagatay Catal (6897842) Görkem Giray (5291287) Bedir Tekinerdogan (6897839) |
| dc.date.none.fl_str_mv | 2023-06-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11219-023-09629-1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Just-in-time_defect_prediction_for_mobile_applications_using_shallow_or_deep_learning_/24997700 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Machine learning Software engineering Just-in-time defect prediction Shallow learning XGBoost Deep learning Imbalanced learning |
| dc.title.none.fl_str_mv | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.</p><h2>Other Information</h2><p dir="ltr">Published in: Software Quality Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11219-023-09629-1" target="_blank">https://dx.doi.org/10.1007/s11219-023-09629-1</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2cfe3ca87abb93082fd644a05fa4b913 |
| identifier_str_mv | 10.1007/s11219-023-09629-1 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24997700 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Just-in-time defect prediction for mobile applications: using shallow or deep learning?Raymon van Dinter (10521952)Cagatay Catal (6897842)Görkem Giray (5291287)Bedir Tekinerdogan (6897839)Information and computing sciencesMachine learningSoftware engineeringJust-in-time defect predictionShallow learningXGBoostDeep learningImbalanced learning<p dir="ltr">Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.</p><h2>Other Information</h2><p dir="ltr">Published in: Software Quality Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11219-023-09629-1" target="_blank">https://dx.doi.org/10.1007/s11219-023-09629-1</a></p>2023-06-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11219-023-09629-1https://figshare.com/articles/journal_contribution/Just-in-time_defect_prediction_for_mobile_applications_using_shallow_or_deep_learning_/24997700CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249977002023-06-09T03:00:00Z |
| spellingShingle | Just-in-time defect prediction for mobile applications: using shallow or deep learning? Raymon van Dinter (10521952) Information and computing sciences Machine learning Software engineering Just-in-time defect prediction Shallow learning XGBoost Deep learning Imbalanced learning |
| status_str | publishedVersion |
| title | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| title_full | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| title_fullStr | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| title_full_unstemmed | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| title_short | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| title_sort | Just-in-time defect prediction for mobile applications: using shallow or deep learning? |
| topic | Information and computing sciences Machine learning Software engineering Just-in-time defect prediction Shallow learning XGBoost Deep learning Imbalanced learning |