Challenges of Using Machine Learning Algorithms for Evaluating and Predicting Software Defects

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dc.citation.epage221
dc.citation.journalTitleСучасні проблеми в радіоелектроніці, телекомунікаціях(СПРТ’2024) : матеріали Міжнародної науково-технічної конференції
dc.citation.spage218
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorYakovyna, V.
dc.contributor.authorKhil, O.
dc.coverage.placenameЛьвів
dc.coverage.temporal23-24 травня 2024 року
dc.coverage.temporal23-24 May 2024
dc.date.accessioned2024-06-25T09:29:18Z
dc.date.available2024-06-25T09:29:18Z
dc.date.created2024-05-23
dc.date.issued2024-05-23
dc.format.extent218-221
dc.format.pages4
dc.identifier.citationYakovyna V. Challenges of Using Machine Learning Algorithms for Evaluating and Predicting Software Defects / V. Yakovyna, O. Khil // Сучасні проблеми в радіоелектроніці, телекомунікаціях(СПРТ’2024) : матеріали Міжнародної науково-технічної конференції, 23-24 May 2024. — Lviv Politechnic Publishing House, 2024. — P. 218–221. — (Information Systems and Technologies. Automated Control Systems. Robotics.).
dc.identifier.citationenYakovyna V. Challenges of Using Machine Learning Algorithms for Evaluating and Predicting Software Defects / V. Yakovyna, O. Khil // Suchasni problemy v radioelektronitsi, telekomunikatsiiakh(SPRT2024) : materialy Mizhnarodnoi naukovo-tekhnichnoi konferentsii, 23-24 May 2024. — Lviv Politechnic Publishing House, 2024. — P. 218–221. — (Information Systems and Technologies. Automated Control Systems. Robotics.).
dc.identifier.isbn978-966-941-945-3
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/62261
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofСучасні проблеми в радіоелектроніці, телекомунікаціях(СПРТ’2024) : матеріали Міжнародної науково-технічної конференції, 2024
dc.relation.references1. Bhat, N.A., Farooq, S.U. An empirical evaluation of defect prediction approaches in within-project and cross-project context. Software Qual J 31, 917–946 (2023). https://doi.org/10.1007/s11219-023-09615-7
dc.relation.references2. Jing, XY., Chen, H., Xu, B. (2023). Cross-Project Defect Prediction. In: Intelligent Software Defect Prediction. Springer, Singapore. https://doi.org/10.1007/978-981-99-2842-2_4
dc.relation.references3. Wu, J., Wu , Y., Niu, N. et al. MHCPDP: multi-source heterogeneous cross-project defect prediction via multi-source transfer learning and autoencoder. Software Qual J 29, 405–430 (2021). https://doi.org/10.1007/s11219-021-09553-2
dc.relation.references4. Lima, M., Valle, V., Costa, E., Lira, F., & Gadelha, B. (2019). Software engineering repositories: expanding the promise database. In Proceedings of the XXXIII Brazilian Symposium on Software Engineering (pp. 427-436). https://doi.org/10.1145/3350768.3350776
dc.relation.references5. Siddiqui, T., Mustaqeem, M. Performance evaluation of software defect prediction with NASA dataset using machine learning techniques. Int. j. inf. tecnol. 15, 4131–4139 (2023). https://doi.org/10.1007/s41870-023-01528-9
dc.relation.references6. Malhotra, R., Chawla, S. & Sharma, A. Software defect prediction using hybrid techniques: a systematic literature review. Soft Comput 27, 8255–8288 (2023). https://doi.org/10.1007/s00500-022-07738-w
dc.relation.references7. Niu, L., Wan, J., Wang, H. et al. Cost-sensitive Dictionary Learning for Software Defect Prediction. Neural Process Lett 52, 2415–2449 (2020). https://doi.org/10.1007/s11063-020-10355-z
dc.relation.references8. Omondiagbe, Osayande Pascal (2022). Dataset For Software Defect Predictions. figshare. Dataset. https://doi.org/10.6084/m9.figshare.19516858.v1
dc.relation.references9. Esteves, G., Figueiredo, E., Veloso, A. et al. Understanding machine learning software defect predictions. Autom Softw Eng 27, 369–392 (2020). https://doi.org/10.1007/s10515-020-00277-4
dc.relation.references10.Khalid A, Badshah G, Ayub N, Shiraz M, Ghouse M. Software Defect Prediction Analysis Using Machine Learning Techniques. Sustainability. 2023; 15(6):5517. https://doi.org/10.3390/su15065517
dc.relation.referencesen1. Bhat, N.A., Farooq, S.U. An empirical evaluation of defect prediction approaches in within-project and cross-project context. Software Qual J 31, 917–946 (2023). https://doi.org/10.1007/s11219-023-09615-7
dc.relation.referencesen2. Jing, XY., Chen, H., Xu, B. (2023). Cross-Project Defect Prediction. In: Intelligent Software Defect Prediction. Springer, Singapore. https://doi.org/10.1007/978-981-99-2842-2_4
dc.relation.referencesen3. Wu, J., Wu , Y., Niu, N. et al. MHCPDP: multi-source heterogeneous cross-project defect prediction via multi-source transfer learning and autoencoder. Software Qual J 29, 405–430 (2021). https://doi.org/10.1007/s11219-021-09553-2
dc.relation.referencesen4. Lima, M., Valle, V., Costa, E., Lira, F., & Gadelha, B. (2019). Software engineering repositories: expanding the promise database. In Proceedings of the XXXIII Brazilian Symposium on Software Engineering (pp. 427-436). https://doi.org/10.1145/3350768.3350776
dc.relation.referencesen5. Siddiqui, T., Mustaqeem, M. Performance evaluation of software defect prediction with NASA dataset using machine learning techniques. Int. j. inf. tecnol. 15, 4131–4139 (2023). https://doi.org/10.1007/s41870-023-01528-9
dc.relation.referencesen6. Malhotra, R., Chawla, S. & Sharma, A. Software defect prediction using hybrid techniques: a systematic literature review. Soft Comput 27, 8255–8288 (2023). https://doi.org/10.1007/s00500-022-07738-w
dc.relation.referencesen7. Niu, L., Wan, J., Wang, H. et al. Cost-sensitive Dictionary Learning for Software Defect Prediction. Neural Process Lett 52, 2415–2449 (2020). https://doi.org/10.1007/s11063-020-10355-z
dc.relation.referencesen8. Omondiagbe, Osayande Pascal (2022). Dataset For Software Defect Predictions. figshare. Dataset. https://doi.org/10.6084/m9.figshare.19516858.v1
dc.relation.referencesen9. Esteves, G., Figueiredo, E., Veloso, A. et al. Understanding machine learning software defect predictions. Autom Softw Eng 27, 369–392 (2020). https://doi.org/10.1007/s10515-020-00277-4
dc.relation.referencesen10.Khalid A, Badshah G, Ayub N, Shiraz M, Ghouse M. Software Defect Prediction Analysis Using Machine Learning Techniques. Sustainability. 2023; 15(6):5517. https://doi.org/10.3390/su15065517
dc.relation.urihttps://doi.org/10.1007/s11219-023-09615-7
dc.relation.urihttps://doi.org/10.1007/978-981-99-2842-2_4
dc.relation.urihttps://doi.org/10.1007/s11219-021-09553-2
dc.relation.urihttps://doi.org/10.1145/3350768.3350776
dc.relation.urihttps://doi.org/10.1007/s41870-023-01528-9
dc.relation.urihttps://doi.org/10.1007/s00500-022-07738-w
dc.relation.urihttps://doi.org/10.1007/s11063-020-10355-z
dc.relation.urihttps://doi.org/10.6084/m9.figshare.19516858.v1
dc.relation.urihttps://doi.org/10.1007/s10515-020-00277-4
dc.relation.urihttps://doi.org/10.3390/su15065517
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.titleChallenges of Using Machine Learning Algorithms for Evaluating and Predicting Software Defects
dc.typeArticle

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Сучасні проблеми в радіоелектроніці, телекомунікаціях (СПРТ'2024) = Advanced Trends in Radioelectronics and Telecommunications (ATRT'2024)