{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:46:03Z","timestamp":1773938763402,"version":"3.50.1"},"reference-count":182,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00521-024-10937-1","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T06:35:09Z","timestamp":1735626909000},"page":"2113-2144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Survey of software defect prediction features"],"prefix":"10.1007","volume":"37","author":[{"given":"Shaoming","family":"Qiu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9723-5336","authenticated-orcid":false,"given":"Bicong","family":"E","sequence":"additional","affiliation":[]},{"given":"Jingjie","family":"He","sequence":"additional","affiliation":[]},{"given":"Liangyu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"10937_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2015.10.009","volume":"90","author":"ZA Rana","year":"2015","unstructured":"Rana ZA, Mian MA, Shamail S (2015) Improving Recall of software defect prediction models using association mining. Knowl-Based Syst 90:1\u201313","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"10937_CR2","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1109\/TSE.2016.2550458","volume":"42","author":"T Lee","year":"2016","unstructured":"Lee T, Nam J, Han D, Kim S, Peter In H (2016) Developer Micro Interaction Metrics for Software Defect Prediction. IEEE Trans Software Eng 42(11):1015\u20131035. https:\/\/doi.org\/10.1109\/TSE.2016.2550458","journal-title":"IEEE Trans Software Eng"},{"key":"10937_CR3","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.eswa.2018.07.042","volume":"114","author":"Y Shao","year":"2018","unstructured":"Shao Y, Liu B, Wang S, Li G (2018) A novel software defect prediction based on atomic class-association rule mining. Expert Syst Appl 114:237\u2013254. https:\/\/doi.org\/10.1016\/j.eswa.2018.07.042","journal-title":"Expert Syst Appl"},{"issue":"10","key":"10937_CR4","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.3390\/app8101902","volume":"8","author":"T Berani\u010d","year":"2018","unstructured":"Berani\u010d T, Podgorelec V, Heri\u010dko M (2018) Towards a reliable identification of deficient code with a combination of software metrics. Appl Sci-Basel 8(10):1902. https:\/\/doi.org\/10.3390\/app8101902","journal-title":"Appl Sci-Basel"},{"key":"10937_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5558561","author":"Y Shao","year":"2021","unstructured":"Shao Y, Zhao J, Wang X, Wu W, Fang J (2021) Research on cross-company defect prediction method to improve software security. Security Commun Netw. https:\/\/doi.org\/10.1155\/2021\/5558561","journal-title":"Security Commun Netw"},{"issue":"12","key":"10937_CR6","doi-asserted-by":"publisher","first-page":"5030","DOI":"10.1109\/TSE.2021.3131950","volume":"48","author":"L Gong","year":"2022","unstructured":"Gong L, Rajbahadur GK, Hassan AE, Jiang S (2022) Revisiting the impact of dependency network metrics on software defect prediction. IEEE Trans Software Eng 48(12):5030\u20135049. https:\/\/doi.org\/10.1109\/TSE.2021.3131950","journal-title":"IEEE Trans Software Eng"},{"issue":"10","key":"10937_CR7","doi-asserted-by":"publisher","first-page":"4023","DOI":"10.1007\/s00500-015-1738-x","volume":"20","author":"S Chatterjee","year":"2016","unstructured":"Chatterjee S, Maji B (2016) A new fuzzy rule based algorithm for estimating software faults in early phase of development. Soft Comput 20(10):4023\u20134035. https:\/\/doi.org\/10.1007\/s00500-015-1738-x","journal-title":"Soft Comput"},{"issue":"4","key":"10937_CR8","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.jksuci.2019.03.006","volume":"32","author":"W Rhmann","year":"2020","unstructured":"Rhmann W, Pandey B, Ansari G, Pandey DK (2020) Software fault prediction based on change metrics using hybrid algorithms: An empirical study. J King Saud Univ - Comput Inf Sci 32(4):419\u2013424. https:\/\/doi.org\/10.1016\/j.jksuci.2019.03.006","journal-title":"J King Saud Univ - Comput Inf Sci"},{"issue":"09","key":"10937_CR9","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1142\/S0218194014400105","volume":"24","author":"K Gao","year":"2014","unstructured":"Gao K, Khoshgoftaar TM, Napolitano A (2014) The use of ensemble-based data preprocessing techniques for software defect prediction. Int J Software Eng Knowl Eng 24(09):1229\u20131253. https:\/\/doi.org\/10.1142\/S0218194014400105","journal-title":"Int J Software Eng Knowl Eng"},{"issue":"1","key":"10937_CR10","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TR.2015.2461676","volume":"65","author":"W Liu","year":"2016","unstructured":"Liu W, Liu S, Gu Q, Chen J, Chen X, Chen D (2016) Empirical studies of a two-stage data preprocessing approach for software fault prediction. IEEE Trans Reliab 65(1):38\u201353. https:\/\/doi.org\/10.1109\/TR.2015.2461676","journal-title":"IEEE Trans Reliab"},{"issue":"4","key":"10937_CR11","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.1007\/s13369-020-04445-2","volume":"45","author":"Sikka G Aarti","year":"2020","unstructured":"Aarti Sikka G, Dhir R (2020) Novel grey relational feature extraction algorithm for software fault-proneness using BBO (B-GRA). Arab J Sci Eng 45(4):2645\u20132662. https:\/\/doi.org\/10.1007\/s13369-020-04445-2","journal-title":"Arab J Sci Eng"},{"issue":"1","key":"10937_CR12","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s10515-021-00311-z","volume":"29","author":"A Balaram","year":"2021","unstructured":"Balaram A, Vasundra S (2021) Prediction of software fault-prone classes using ensemble random forest with adaptive synthetic sampling algorithm. Autom Softw Eng 29(1):6. https:\/\/doi.org\/10.1007\/s10515-021-00311-z","journal-title":"Autom Softw Eng"},{"key":"10937_CR13","doi-asserted-by":"publisher","unstructured":"Anand K, Jena AK, Choudhary T Performance analysis of feature selection techniques in software defect prediction using machine learning. In: 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), pp. 1\u20137. https:\/\/doi.org\/10.1109\/ASSIC55218.2022.10088364","DOI":"10.1109\/ASSIC55218.2022.10088364"},{"issue":"8","key":"10937_CR14","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1016\/j.infsof.2013.02.009","volume":"55","author":"D Radjenovi\u0107","year":"2013","unstructured":"Radjenovi\u0107 D, Heri\u010dko M, Torkar R, \u017divkovi\u010d A (2013) Software fault prediction metrics: A systematic literature review. Inf Softw Technol 55(8):1397\u20131418. https:\/\/doi.org\/10.1016\/j.infsof.2013.02.009","journal-title":"Inf Softw Technol"},{"key":"10937_CR15","doi-asserted-by":"publisher","unstructured":"Chidamber SR, Kemerer CF (1994) A metrics suite for object oriented design. IEEE Trans Software Eng, ( Conference Name: IEEE Transactions on Software Engineering), 20(6):476\u2013493. https:\/\/doi.org\/10.1109\/32.295895","DOI":"10.1109\/32.295895"},{"issue":"2","key":"10937_CR16","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/sym11020212","volume":"11","author":"L Son","year":"2019","unstructured":"Son L, Pritam N, Khari M, Kumar R, Phuong P, Thong P (2019) Empirical study of software defect prediction: a systematic mapping. Symmetry-Basel 11(2):212. https:\/\/doi.org\/10.3390\/sym11020212","journal-title":"Symmetry-Basel"},{"issue":"5","key":"10937_CR17","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/s10664-021-09991-3","volume":"26","author":"A Tahir","year":"2021","unstructured":"Tahir A, Bennin KE, Xiao X, MacDonell SG (2021) Does class size matter? An in-depth assessment of the effect of class size in software defect prediction. Empir Softw Eng 26(5):106. https:\/\/doi.org\/10.1007\/s10664-021-09991-3","journal-title":"Empir Softw Eng"},{"key":"10937_CR18","doi-asserted-by":"publisher","first-page":"170548","DOI":"10.1109\/ACCESS.2020.3022087","volume":"8","author":"SR Aziz","year":"2020","unstructured":"Aziz SR, Khan TA, Nadeem A (2020) Efficacy of inheritance aspect in software fault prediction-a survey paper. IEEE Access 8:170548\u2013170567. https:\/\/doi.org\/10.1109\/ACCESS.2020.3022087","journal-title":"IEEE Access"},{"issue":"2","key":"10937_CR19","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s10462-017-9563-5","volume":"51","author":"SS Rathore","year":"2019","unstructured":"Rathore SS, Kumar S (2019) A study on software fault prediction techniques. Artif Intell Rev 51(2):255\u2013327. https:\/\/doi.org\/10.1007\/s10462-017-9563-5","journal-title":"Artif Intell Rev"},{"issue":"12","key":"10937_CR20","doi-asserted-by":"publisher","first-page":"8255","DOI":"10.1007\/s00500-022-07738-w","volume":"27","author":"R Malhotra","year":"2023","unstructured":"Malhotra R, Chawla S, Sharma A (2023) Software defect prediction using hybrid techniques: a systematic literature review. Soft Comput 27(12):8255\u20138288. https:\/\/doi.org\/10.1007\/s00500-022-07738-w","journal-title":"Soft Comput"},{"issue":"7","key":"10937_CR21","doi-asserted-by":"publisher","first-page":"5723","DOI":"10.1007\/s11831-022-09787-8","volume":"29","author":"M Nevendra","year":"2022","unstructured":"Nevendra M, Singh P (2022) A survey of software defect prediction based on deep learning. Archiv Comput Methods Eng 29(7):5723\u20135748. https:\/\/doi.org\/10.1007\/s11831-022-09787-8","journal-title":"Archiv Comput Methods Eng"},{"key":"10937_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111537","volume":"195","author":"G Giray","year":"2023","unstructured":"Giray G, Bennin KE, K\u00f6ksal \u00d6, Babur \u00d6, Tekinerdogan B (2023) On the use of deep learning in software defect prediction. J Syst Softw 195:111537. https:\/\/doi.org\/10.1016\/j.jss.2022.111537","journal-title":"J Syst Softw"},{"key":"10937_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2023.107175","volume":"158","author":"ZM Zain","year":"2023","unstructured":"Zain ZM, Sakri S, Ismail NHA (2023) Application of deep learning in software defect prediction: Systematic literature review and meta-analysis. Inf Software Technol 158:107175. https:\/\/doi.org\/10.1016\/j.infsof.2023.107175","journal-title":"Inf Software Technol"},{"key":"10937_CR24","doi-asserted-by":"publisher","unstructured":"McCabe TJ (1976) A complexity measure. IEEE Trans Software Eng SE\u20132(4):308\u2013320. https:\/\/doi.org\/10.1109\/TSE.1976.233837","DOI":"10.1109\/TSE.1976.233837"},{"key":"10937_CR25","unstructured":"Halstead MH (1979) Elements of Software Science, 3. print edn. Operating and programming systems series, vol. 2. North Holland, New York"},{"issue":"1","key":"10937_CR26","first-page":"151","volume":"12","author":"R Martin","year":"1994","unstructured":"Martin R (1994) OO design quality metrics. Analy Dependencies 12(1):151\u2013170","journal-title":"Analy Dependencies"},{"issue":"1","key":"10937_CR27","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/32.979986","volume":"28","author":"J Bansiya","year":"2002","unstructured":"Bansiya J, Davis CG (2002) A hierarchical model for object-oriented design quality assessment. IEEE Trans Software Eng 28(1):4\u201317. https:\/\/doi.org\/10.1109\/32.979986","journal-title":"IEEE Trans Software Eng"},{"key":"10937_CR28","doi-asserted-by":"publisher","unstructured":"Tang M-H, Kao M-H, Chen M-H (1999) An empirical study on object-oriented metrics. In: Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403), pp. 242\u2013249. https:\/\/doi.org\/10.1109\/METRIC.1999.809745","DOI":"10.1109\/METRIC.1999.809745"},{"key":"10937_CR29","unstructured":"Hitz M, Montazeri B (1995) Measuring coupling and cohesion in object-oriented systems. In: Proc. Int. Symposium on Applied Corporate Computing, Oct. 25-27"},{"key":"10937_CR30","volume-title":"Object-oriented Software Metrics: a Practical Guide","author":"M Lorenz","year":"1994","unstructured":"Lorenz M, Kidd J (1994) Object-oriented Software Metrics: a Practical Guide. Prentice Hall object-oriented series, PTR Prentice Hall, Englewood Cliffs"},{"issue":"5","key":"10937_CR31","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1109\/TSE.1981.231113","volume":"7","author":"S Henry","year":"1981","unstructured":"Henry S, Kafura D (1981) Software structure metrics based on information flow. IEEE Trans Software Eng SE 7(5):510\u2013518. https:\/\/doi.org\/10.1109\/TSE.1981.231113","journal-title":"IEEE Trans Software Eng SE"},{"issue":"3","key":"10937_CR32","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.peva.2012.09.004","volume":"70","author":"D Cotroneo","year":"2013","unstructured":"Cotroneo D, Natella R, Pietrantuono R (2013) Predicting aging-related bugs using software complexity metrics. Perform Eval 70(3):163\u2013178. https:\/\/doi.org\/10.1016\/j.peva.2012.09.004","journal-title":"Perform Eval"},{"key":"10937_CR33","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s11219-012-9180-0","volume":"21","author":"G \u00c7al\u0131kl\u0131","year":"2013","unstructured":"\u00c7al\u0131kl\u0131 G, Bener AB (2013) Influence of confirmation biases of developers on software quality: an empirical study. Software Qual J 21:377\u2013416","journal-title":"Software Qual J"},{"issue":"9","key":"10937_CR34","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ipl.2014.03.012","volume":"114","author":"Y Ma","year":"2014","unstructured":"Ma Y, Zhu S, Qin K, Luo G (2014) Combining the requirement information for software defect estimation in design time. Inf Process Lett 114(9):469\u2013474. https:\/\/doi.org\/10.1016\/j.ipl.2014.03.012","journal-title":"Inf Process Lett"},{"issue":"12","key":"10937_CR35","doi-asserted-by":"publisher","first-page":"963","DOI":"10.3390\/e20120963","volume":"20","author":"A Kaur","year":"2018","unstructured":"Kaur A, Chopra D (2018) Entropy churn metrics for fault prediction in software systems. Entropy 20(12):963. https:\/\/doi.org\/10.3390\/e20120963","journal-title":"Entropy"},{"key":"10937_CR36","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.infsof.2014.09.006","volume":"57","author":"Y Zhao","year":"2015","unstructured":"Zhao Y, Yang Y, Lu H, Zhou Y, Song Q, Xu B (2015) An empirical analysis of package-modularization metrics: Implications for software fault-proneness. Inf Softw Technol 57:186\u2013203","journal-title":"Inf Softw Technol"},{"issue":"3","key":"10937_CR37","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1049\/iet-sen.2018.5439","volume":"14","author":"Q Yu","year":"2020","unstructured":"Yu Q, Jiang S, Qian J, Bo L, Jiang L, Zhang G (2020) Process metrics for software defect prediction in object-oriented programs. IET Software 14(3):283\u2013292. https:\/\/doi.org\/10.1049\/iet-sen.2018.5439","journal-title":"IET Software"},{"issue":"03","key":"10937_CR38","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1142\/S0218194021500108","volume":"31","author":"X Yang","year":"2021","unstructured":"Yang X, Yu H, Fan G, Yang K (2021) DEJIT: a differential evolution algorithm for effort-aware just-in-time software defect prediction. Int J Software Eng Knowl Eng 31(03):289\u2013310. https:\/\/doi.org\/10.1142\/S0218194021500108","journal-title":"Int J Software Eng Knowl Eng"},{"key":"10937_CR39","doi-asserted-by":"publisher","first-page":"590","DOI":"10.7717\/peerj-cs.590","volume":"7","author":"R Muhammad","year":"2021","unstructured":"Muhammad R, Nadeem A, Sindhu MA (2021) Vovel metrics-novel coupling metrics for improved software fault prediction. PeerJ Comput Sci 7:590. https:\/\/doi.org\/10.7717\/peerj-cs.590","journal-title":"PeerJ Comput Sci"},{"key":"10937_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/6230953","volume":"2019","author":"G Fan","year":"2019","unstructured":"Fan G, Diao X, Yu H, Yang K, Chen L (2019) Software Defect Prediction via Attention-Based Recurrent Neural Network. Sci Program 2019:1\u201314. https:\/\/doi.org\/10.1155\/2019\/6230953","journal-title":"Sci Program"},{"key":"10937_CR41","doi-asserted-by":"publisher","first-page":"83812","DOI":"10.1109\/ACCESS.2019.2925313","volume":"7","author":"H Liang","year":"2019","unstructured":"Liang H, Yu Y, Jiang L, Xie Z (2019) Seml: a semantic lstm model for software defect prediction. IEEE Access 7:83812\u201383824. https:\/\/doi.org\/10.1109\/ACCESS.2019.2925313","journal-title":"IEEE Access"},{"key":"10937_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/6038619","volume":"2020","author":"M Shi","year":"2020","unstructured":"Shi M, He P, Xiao H, Li H, Zeng C (2020) An approach to semantic and structural features learning for software defect prediction. Math Probl Eng 2020:1\u201313. https:\/\/doi.org\/10.1155\/2020\/6038619","journal-title":"Math Probl Eng"},{"issue":"12","key":"10937_CR43","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TSE.2018.2877612","volume":"46","author":"S Wang","year":"2020","unstructured":"Wang S, Liu T, Nam J, Tan L (2020) Deep semantic feature learning for software defect prediction. IEEE Trans Software Eng 46(12):1267\u20131293. https:\/\/doi.org\/10.1109\/TSE.2018.2877612","journal-title":"IEEE Trans Software Eng"},{"key":"10937_CR44","doi-asserted-by":"publisher","first-page":"739","DOI":"10.7717\/peerj-cs.739","volume":"7","author":"AB Farid","year":"2021","unstructured":"Farid AB, Fathy EM, Eldin AS, Abd-Elmegid LA (2021) Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). PeerJ Comput Sci 7:739. https:\/\/doi.org\/10.7717\/peerj-cs.739","journal-title":"PeerJ Comput Sci"},{"issue":"2","key":"10937_CR45","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/TR.2020.3047396","volume":"70","author":"H Wang","year":"2021","unstructured":"Wang H, Zhuang W, Zhang X (2021) Software defect prediction based on gated hierarchical LSTMs. IEEE Trans Reliab 70(2):711\u2013727. https:\/\/doi.org\/10.1109\/TR.2020.3047396","journal-title":"IEEE Trans Reliab"},{"issue":"09","key":"10937_CR46","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1142\/S0218194022500504","volume":"32","author":"C Tao","year":"2022","unstructured":"Tao C, Wang T, Guo H, Zhang J (2022) An approach to software defect prediction combining semantic features and code changes. Int J Software Eng Knowl Eng 32(09):1345\u20131368. https:\/\/doi.org\/10.1142\/S0218194022500504","journal-title":"Int J Software Eng Knowl Eng"},{"key":"10937_CR47","doi-asserted-by":"publisher","unstructured":"Wang T, Tao C, Guo H, Tang L Semantic feature learning based on double sequences structure for software defect number prediction. In: 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), pp. 157\u2013166. https:\/\/doi.org\/10.1109\/QRS57517.2022.00026","DOI":"10.1109\/QRS57517.2022.00026"},{"key":"10937_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108852","volume":"248","author":"W Zhuang","year":"2022","unstructured":"Zhuang W, Wang H, Zhang X (2022) Just-in-time defect prediction based on AST change embedding. Knowledge-Based Syst 248:108852. https:\/\/doi.org\/10.1016\/j.knosys.2022.108852","journal-title":"Knowledge-Based Syst"},{"key":"10937_CR49","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.patrec.2022.04.039","volume":"160","author":"Y Xing","year":"2022","unstructured":"Xing Y, Qian X, Guan Y, Yang B, Zhang Y (2022) Cross-project defect prediction based on g-LSTM model. Pattern Recognition Lett 160:50\u201357. https:\/\/doi.org\/10.1016\/j.patrec.2022.04.039","journal-title":"Pattern Recognition Lett"},{"key":"10937_CR50","doi-asserted-by":"publisher","unstructured":"Yu T-Y, Huang C-Y, Fang NC Use of deep learning model with attention mechanism for software fault prediction. In: 2021 8th International Conference on Dependable Systems and Their Applications (DSA), pp. 161\u2013171. https:\/\/doi.org\/10.1109\/DSA52907.2021.00025","DOI":"10.1109\/DSA52907.2021.00025"},{"issue":"2","key":"10937_CR51","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/TR.2020.3040191","volume":"70","author":"J Xu","year":"2020","unstructured":"Xu J, Wang F, Ai J (2020) Defect prediction with semantics and context features of codes based on graph representation learning. IEEE Trans Reliabil 70(2):613\u2013625. https:\/\/doi.org\/10.1109\/TR.2020.3040191","journal-title":"IEEE Trans Reliabil"},{"key":"10937_CR52","doi-asserted-by":"publisher","unstructured":"Zhang Q, Wu B Software, defect prediction via transformer. In: (2020) IEEE 4th Information Technology, Networking. Electronic and Automation Control Conference (ITNEC) 1:874\u2013879. https:\/\/doi.org\/10.1109\/ITNEC48623.2020.9084745","DOI":"10.1109\/ITNEC48623.2020.9084745"},{"key":"10937_CR53","doi-asserted-by":"publisher","first-page":"55241","DOI":"10.1109\/ACCESS.2020.2981869","volume":"8","author":"L Sheng","year":"2020","unstructured":"Sheng L, Lu L, Lin J (2020) An adversarial discriminative convolutional neural network for cross-project defect prediction. IEEE Access 8:55241\u201355253. https:\/\/doi.org\/10.1109\/ACCESS.2020.2981869","journal-title":"IEEE Access"},{"key":"10937_CR54","doi-asserted-by":"publisher","first-page":"170844","DOI":"10.1109\/ACCESS.2019.2953696","volume":"7","author":"Z Cai","year":"2019","unstructured":"Cai Z, Lu L, Qiu S (2019) An abstract syntax tree encoding method for cross-project defect prediction. IEEE Access 7:170844\u2013170853. https:\/\/doi.org\/10.1109\/ACCESS.2019.2953696","journal-title":"IEEE Access"},{"key":"10937_CR55","doi-asserted-by":"publisher","unstructured":"Wang S, Liu T, Tan L Automatically learning semantic features for defect prediction. In: Proceedings of the 38th International Conference on Software Engineering, pp. 297\u2013308. ACM. https:\/\/doi.org\/10.1145\/2884781.2884804","DOI":"10.1145\/2884781.2884804"},{"key":"10937_CR56","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.infsof.2018.10.001","volume":"106","author":"T Shippey","year":"2019","unstructured":"Shippey T, Bowes D, Hall T (2019) Automatically identifying code features for software defect prediction: Using AST N-grams. Inf Softw Technol 106:142\u2013160. https:\/\/doi.org\/10.1016\/j.infsof.2018.10.001","journal-title":"Inf Softw Technol"},{"key":"10937_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.cola.2020.100979","volume":"59","author":"K Shi","year":"2020","unstructured":"Shi K, Lu Y, Chang J, Wei Z (2020) PathPair2vec: An AST path pair-based code representation method for defect prediction. J Comput Lang 59:100979. https:\/\/doi.org\/10.1016\/j.cola.2020.100979","journal-title":"J Comput Lang"},{"key":"10937_CR58","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.datak.2017.07.003","volume":"114","author":"AV Phan","year":"2018","unstructured":"Phan AV, Chau PN, Nguyen ML, Bui LT (2018) Automatically classifying source code using tree-based approaches. Data Knowledge Eng 114:12\u201325. https:\/\/doi.org\/10.1016\/j.datak.2017.07.003","journal-title":"Data Knowledge Eng"},{"key":"10937_CR59","doi-asserted-by":"publisher","unstructured":"Tang L, Tao C, Guo H, Zhang J Software defect prediction via GCN based on structural and context information. In: 2022 9th International Conference on Dependable Systems and Their Applications (DSA), pp. 310\u2013319. https:\/\/doi.org\/10.1109\/DSA56465.2022.00049","DOI":"10.1109\/DSA56465.2022.00049"},{"key":"10937_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113156","volume":"147","author":"A Majd","year":"2020","unstructured":"Majd A, Vahidi-Asl M, Khalilian A, Poorsarvi-Tehrani P, Haghighi H (2020) SLDeep: Statement-level software defect prediction using deep-learning model on static code features. Expert Syst Appl 147:113156. https:\/\/doi.org\/10.1016\/j.eswa.2019.113156","journal-title":"Expert Syst Appl"},{"issue":"3","key":"10937_CR61","doi-asserted-by":"publisher","first-page":"0247444","DOI":"10.1371\/journal.pone.0247444","volume":"16","author":"HS Munir","year":"2021","unstructured":"Munir HS, Ren S, Mustafa M, Siddique CN, Qayyum S (2021) Attention based GRU-LSTM for software defect prediction. PLoS One 16(3):0247444. https:\/\/doi.org\/10.1371\/journal.pone.0247444","journal-title":"PLoS One"},{"issue":"11","key":"10937_CR62","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/TSE.2018.2876256","volume":"46","author":"M Wen","year":"2020","unstructured":"Wen M, Wu R, Cheung S-C (2020) How well do change sequences predict defects? sequence learning from software changes. IEEE Trans Software Eng 46(11):1155\u20131175. https:\/\/doi.org\/10.1109\/TSE.2018.2876256","journal-title":"IEEE Trans Software Eng"},{"key":"10937_CR63","doi-asserted-by":"publisher","first-page":"108870","DOI":"10.1109\/ACCESS.2022.3213844","volume":"10","author":"M Gupta","year":"2022","unstructured":"Gupta M, Rajnish K, Bhattacharjee V (2022) Cognitive complexity and graph convolutional approach over control flow graph for software defect prediction. IEEE Access 10:108870\u2013108894. https:\/\/doi.org\/10.1109\/ACCESS.2022.3213844","journal-title":"IEEE Access"},{"key":"10937_CR64","doi-asserted-by":"publisher","unstructured":"Wang X, Lu L, Wang B, Shang Y, Yang H Software defect prediction via GIN with hybrid graphical features. In: 2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C), pp. 411\u2013416. https:\/\/doi.org\/10.1109\/QRS-C57518.2022.00066","DOI":"10.1109\/QRS-C57518.2022.00066"},{"key":"10937_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.111108","volume":"184","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Yang B, Li G, Liu H, Jin Z (2020) Precise learning of source code contextual semantics via hierarchical dependence structure and graph attention networks. J Syst Software 184:111108. https:\/\/doi.org\/10.1016\/j.jss.2021.111108","journal-title":"J Syst Software"},{"issue":"16","key":"10937_CR66","doi-asserted-by":"publisher","first-page":"7877","DOI":"10.1007\/s00500-022-06830-5","volume":"26","author":"MN Uddin","year":"2022","unstructured":"Uddin MN, Li B, Ali Z, Kefalas P, Khan I, Zada I (2022) Software defect prediction employing BiLSTM and BERT-based semantic feature. Soft Comput 26(16):7877\u20137891. https:\/\/doi.org\/10.1007\/s00500-022-06830-5","journal-title":"Soft Comput"},{"key":"10937_CR67","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"10937_CR68","doi-asserted-by":"publisher","unstructured":"Chen J, Hu K, Yu Y, Chen Z, Xuan Q, Liu Y, Filkov V Software visualization and deep transfer learning for effective software defect prediction. In: Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. ICSE \u201920. Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3377811.3380389. event-place: Seoul, South Korea","DOI":"10.1145\/3377811.3380389"},{"issue":"3","key":"10937_CR69","doi-asserted-by":"publisher","first-page":"5251","DOI":"10.32604\/cmc.2022.026750","volume":"72","author":"Y Chen","year":"2022","unstructured":"Chen Y, Xu C, He JS, Xiao S, Shen F (2022) Compiler IR-Based Program Encoding Method for Software Defect Prediction. Cmc-Comput Mater Contin 72(3):5251\u20135272. https:\/\/doi.org\/10.32604\/cmc.2022.026750","journal-title":"Cmc-Comput Mater Contin"},{"key":"10937_CR70","doi-asserted-by":"publisher","unstructured":"Phan AV, Le\u00a0Nguyen M (2017) Convolutional neural networks on assembly code for predicting software defects. In: 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), pp. 37\u201342. IEEE, Hanoi. https:\/\/doi.org\/10.1109\/IESYS.2017.8233558","DOI":"10.1109\/IESYS.2017.8233558"},{"key":"10937_CR71","volume":"2013","author":"P He","year":"2013","unstructured":"He P, Li B, Ma Y, He L (2013) Using software dependency to bug prediction. Math Problems Eng Math Problems Eng 2013:869356","journal-title":"Math Problems Eng Math Problems Eng"},{"key":"10937_CR72","doi-asserted-by":"publisher","unstructured":"Prateek S, Pasala A, Aracena LM Evaluating performance of network metrics for bug prediction in software. In: 2013 20th Asia-Pacific Software Engineering Conference (APSEC), 1, 124\u2013131. https:\/\/doi.org\/10.1109\/APSEC.2013.27","DOI":"10.1109\/APSEC.2013.27"},{"key":"10937_CR73","doi-asserted-by":"publisher","unstructured":"Gao H, Lu M, Pan C, Xu B Empirical study: Are complex network features suitable for cross-version software defect prediction? In: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICSESS47205.2019.9040793","DOI":"10.1109\/ICSESS47205.2019.9040793"},{"key":"10937_CR74","doi-asserted-by":"publisher","unstructured":"Xu J, Shang J, Huang Z CFIWSE: A hybrid preprocessing approach for defect prediction on imbalance real-world datasets. In: 2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C), pp. 392\u2013401. https:\/\/doi.org\/10.1109\/QRS-C57518.2022.00064","DOI":"10.1109\/QRS-C57518.2022.00064"},{"key":"10937_CR75","doi-asserted-by":"publisher","unstructured":"Zimmermann T, Nagappan N (2008) Predicting defects using network analysis on dependency graphs. In: Proceedings of the 30th International Conference on Software Engineering. ICSE \u201908, pp. 531\u2013540. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1368088.1368161","DOI":"10.1145\/1368088.1368161"},{"issue":"12","key":"10937_CR76","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-015-5426-3","volume":"59","author":"L Chen","year":"2016","unstructured":"Chen L, Ma W, Zhou Y, Xu L, Wang Z, Chen Z, Xu B (2016) Empirical analysis of network measures for predicting high severity software faults. Sci CHINA Inf Sci 59(12):122901. https:\/\/doi.org\/10.1007\/s11432-015-5426-3","journal-title":"Sci CHINA Inf Sci"},{"issue":"12","key":"10937_CR77","doi-asserted-by":"publisher","first-page":"5030","DOI":"10.1109\/TSE.2021.3131950","volume":"48","author":"L Gong","year":"2022","unstructured":"Gong L, Rajbahadur GK, Hassan AE, Jiang S (2022) Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction. IEEE Trans Software Eng 48(12):5030\u20135049. https:\/\/doi.org\/10.1109\/TSE.2021.3131950","journal-title":"IEEE Trans Software Eng"},{"issue":"2","key":"10937_CR78","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s00766-014-0215-x","volume":"21","author":"J Wang","year":"2016","unstructured":"Wang J, Wang Q (2016) Analyzing and predicting software integration bugs using network analysis on requirements dependency network. Requirements Eng 21(2):161\u2013184. https:\/\/doi.org\/10.1007\/s00766-014-0215-x","journal-title":"Requirements Eng"},{"key":"10937_CR79","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.jss.2015.06.015","volume":"108","author":"Y Qu","year":"2015","unstructured":"Qu Y, Guan X, Zheng Q, Liu T, Wang L, Hou Y, Yang Z (2015) Exploring community structure of software Call Graph and its applications in class cohesion measurement. J Syst Softw 108:193\u2013210. https:\/\/doi.org\/10.1016\/j.jss.2015.06.015","journal-title":"J Syst Softw"},{"issue":"10","key":"10937_CR80","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.3390\/e24101373","volume":"24","author":"M Cui","year":"2022","unstructured":"Cui M, Long S, Jiang Y, Na X (2022) Research of software defect prediction model based on complex network and graph neural network. Entropy 24(10):1373. https:\/\/doi.org\/10.3390\/e24101373","journal-title":"Entropy"},{"issue":"4","key":"10937_CR81","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s10664-021-09965-5","volume":"26","author":"Y Qu","year":"2021","unstructured":"Qu Y, Yin H (2021) Evaluating network embedding techniques\u2019 performances in software bug prediction. Empir Softw Eng 26(4):60. https:\/\/doi.org\/10.1007\/s10664-021-09965-5","journal-title":"Empir Softw Eng"},{"key":"10937_CR82","doi-asserted-by":"publisher","unstructured":"Qu Y, Liu T, Chi J, Jin Y, Cui D, He A, Zheng Q (2018) node2defect: using network embedding to improve software defect prediction. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 844\u2013849. ACM, Montpellier France. https:\/\/doi.org\/10.1145\/3238147.3240469","DOI":"10.1145\/3238147.3240469"},{"key":"10937_CR83","first-page":"279","volume":"65","author":"N Zhang","year":"2020","unstructured":"Zhang N, Zhu K, Ying S, Wang X (2020) Software defect prediction based on stacked contractive autoencoder and multi-objective optimization. Cmc-Comput Mater Contin 65:279\u2013308","journal-title":"Cmc-Comput Mater Contin"},{"key":"10937_CR84","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2022.107057","volume":"152","author":"C Zhou","year":"2022","unstructured":"Zhou C, He P, Zeng C, Ma J (2022) Software defect prediction with semantic and structural information of codes based on Graph Neural Networks. Inf Softw Technol 152:107057. https:\/\/doi.org\/10.1016\/j.infsof.2022.107057","journal-title":"Inf Softw Technol"},{"issue":"6","key":"10937_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2018","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv 50(6):1\u201345. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput Surv"},{"key":"10937_CR86","volume-title":"Machine learning","author":"TM Mitchell","year":"1997","unstructured":"Mitchell TM (1997) Machine learning. McGraw-Hill series in computer science, McGraw-Hill, New York"},{"key":"10937_CR87","doi-asserted-by":"publisher","unstructured":"Kira K, Rendell LA (1992) A practical approach to feature selection. In: Sleeman, D., Edwards, P. (eds.) Machine Learning Proceedings 1992, pp. 249\u2013256. Morgan Kaufmann, San Francisco (CA). https:\/\/doi.org\/10.1016\/B978-1-55860-247-2.50037-1","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"10937_CR88","unstructured":"Han J, Kamber M (2006) Data Mining: Concepts and Techniques, 2nd ed edn. The Morgan Kaufmann series in data management systems. Elsevier ; Morgan Kaufmann, Amsterdam ; Boston : San Francisco, CA"},{"key":"10937_CR89","doi-asserted-by":"publisher","unstructured":"Kononenko I (1994) Estimating attributes: analysis and extensions of relief. In: Bergadano F, De Raedt L (eds) Machine learning: ECML-94. Springer, Berlin, Heidelberg, pp 171\u2013182. https:\/\/doi.org\/10.1007\/3-540-57868-457","DOI":"10.1007\/3-540-57868-457"},{"key":"10937_CR90","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1142\/S0218194015400288","volume":"25","author":"Hj Wang","year":"2015","unstructured":"Wang Hj, Khoshgoftaar TM, Seliya N (2015) On the stability of feature selection methods in software quality prediction: an empirical investigation. Int J Software Eng Knowledge Eng 25:1467\u20131490. https:\/\/doi.org\/10.1142\/S0218194015400288","journal-title":"Int J Software Eng Knowledge Eng"},{"issue":"01","key":"10937_CR91","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1142\/S0218194015400069","volume":"25","author":"K Gao","year":"2015","unstructured":"Gao K, Khoshgoftaar TM, Napolitano A (2015) Investigating two approaches for adding feature ranking to sampled ensemble learning for software quality estimation. Int J Software Eng Knowl Eng 25(01):115\u2013146. https:\/\/doi.org\/10.1142\/S0218194015400069","journal-title":"Int J Software Eng Knowl Eng"},{"issue":"05","key":"10937_CR92","doi-asserted-by":"publisher","first-page":"1360010","DOI":"10.1142\/S0218213013600105","volume":"22","author":"H Wang","year":"2013","unstructured":"Wang H, Khoshgoftaar TM, Liang Qa (2013) A study of software metric selection techniques: stability analysis and defect prediction model performance. Int J Artif Intell Tools 22(05):1360010. https:\/\/doi.org\/10.1142\/S0218213013600105","journal-title":"Int J Artif Intell Tools"},{"key":"10937_CR93","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.infsof.2014.07.005","volume":"58","author":"IH Laradji","year":"2015","unstructured":"Laradji IH, Alshayeb M, Ghouti L (2015) Software defect prediction using ensemble learning on selected features. Inf Softw Technol 58:388\u2013402. https:\/\/doi.org\/10.1016\/j.infsof.2014.07.005","journal-title":"Inf Softw Technol"},{"issue":"3","key":"10937_CR94","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s10115-013-0721-z","volume":"42","author":"G Czibula","year":"2015","unstructured":"Czibula G, Marian Z, Czibula IG (2015) Detecting software design defects using relational association rule mining. Knowl Inf Syst 42(3):545\u2013577. https:\/\/doi.org\/10.1007\/s10115-013-0721-z","journal-title":"Knowl Inf Syst"},{"key":"10937_CR95","first-page":"655","volume":"42","author":"DL Gupta","year":"2017","unstructured":"Gupta DL, Saxena K (2017) Software bug prediction using object-oriented metrics. Sadhana-Academy Proc Eng Sci 42:655\u2013669","journal-title":"Sadhana-Academy Proc Eng Sci"},{"issue":"3","key":"10937_CR96","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.3390\/app12031387","volume":"12","author":"C Li","year":"2022","unstructured":"Li C, Yuan Y, Yang J (2022) Causally remove negative confound effects of size metric for software defect prediction. Appl Sci-Basel 12(3):1387. https:\/\/doi.org\/10.3390\/app12031387","journal-title":"Appl Sci-Basel"},{"issue":"5","key":"10937_CR97","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1631\/FITEE.2100468","volume":"23","author":"J Chen","year":"2022","unstructured":"Chen J, Wang X, Cai S, Xu J, Chen J, Chen H (2022) A software defect prediction method with metric compensation based on feature selection and transfer learning. Front Inf Technol Electron Eng 23(5):715\u2013731. https:\/\/doi.org\/10.1631\/FITEE.2100468","journal-title":"Front Inf Technol Electron Eng"},{"issue":"1","key":"10937_CR98","doi-asserted-by":"publisher","first-page":"282","DOI":"10.2991\/ijcis.2018.125905638","volume":"12","author":"K Bashir","year":"2019","unstructured":"Bashir K, Li T, Yohannese CW (2019) An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework. Int J Comput Intell Syst 12(1):282\u2013298. https:\/\/doi.org\/10.2991\/ijcis.2018.125905638","journal-title":"Int J Comput Intell Syst"},{"issue":"5","key":"10937_CR99","first-page":"721","volume":"17","author":"K Bashir","year":"2020","unstructured":"Bashir K, Li T, Yahaya M (2020) A novel feature selection method based on maximum likelihood logistic regression for imbalanced learning in software defect prediction. Int Arab J Inf Technol 17(5):721\u2013730","journal-title":"Int Arab J Inf Technol"},{"key":"10937_CR100","doi-asserted-by":"publisher","unstructured":"Fan S, Liu C, Li Z An empirical study on the impact of the interaction between feature selection and sampling in defect prediction. In: 2020 7th International Conference on Dependable Systems and Their Applications (DSA), pp. 131\u2013140. https:\/\/doi.org\/10.1109\/DSA51864.2020.00025","DOI":"10.1109\/DSA51864.2020.00025"},{"key":"10937_CR101","doi-asserted-by":"publisher","first-page":"647","DOI":"10.2991\/ijcis.2017.10.1.43","volume":"10","author":"W Chubato","year":"2017","unstructured":"Chubato W, Li T (2017) A combined-learning based framework for improved software fault prediction. Int J Comput Intell Syst 10:647. https:\/\/doi.org\/10.2991\/ijcis.2017.10.1.43","journal-title":"Int J Comput Intell Syst"},{"issue":"13","key":"10937_CR102","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.3390\/app9132764","volume":"9","author":"AO Balogun","year":"2019","unstructured":"Balogun AO, Basri S, Abdulkadir SJ, Hashim AS (2019) Performance analysis of feature selection methods in software defect prediction: a search method approach. Appl Sci-Basel 9(13):2764","journal-title":"Appl Sci-Basel"},{"key":"10937_CR103","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.infsof.2014.11.006","volume":"59","author":"P He","year":"2015","unstructured":"He P, Li B, Liu X, Chen J, Ma Y (2015) An empirical study on software defect prediction with a simplified metric set. Inf Softw Technol 59:170\u2013190. https:\/\/doi.org\/10.1016\/j.infsof.2014.11.006","journal-title":"Inf Softw Technol"},{"issue":"01","key":"10937_CR104","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1142\/S0218194015400057","volume":"25","author":"Wang Hj","year":"2015","unstructured":"Hj Wang, Khoshgoftaar TM, Napolitano A (2015) An empirical investigation on wrapper-based feature selection for predicting software quality. Int J Software Eng Knowl Eng 25(01):93\u2013114. https:\/\/doi.org\/10.1142\/S0218194015400057","journal-title":"Int J Software Eng Knowl Eng"},{"key":"10937_CR105","doi-asserted-by":"publisher","unstructured":"Punitha K, Latha B (2016) Sampling imbalance dataset for software defect prediction using hybrid neuro-fuzzy systems with Naive Bayes classifier. Tehnicki Vjesnik-Techn Gazette 23(6):1795\u20131804. https:\/\/doi.org\/10.17559\/TV-20151219112129","DOI":"10.17559\/TV-20151219112129"},{"key":"10937_CR106","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.jss.2019.03.012","volume":"152","author":"C Ni","year":"2019","unstructured":"Ni C, Chen X, Wu F, Shen Y, Gu Q (2019) An empirical study on pareto based multi-objective feature selection for software defect prediction. J Syst Softw 152:215\u2013238","journal-title":"J Syst Softw"},{"issue":"24","key":"10937_CR107","doi-asserted-by":"publisher","first-page":"13999","DOI":"10.1007\/s00500-022-07445-6","volume":"26","author":"S Goyal","year":"2022","unstructured":"Goyal S (2022) Software fault prediction using evolving populations with mathematical diversification. Soft Comput 26(24):13999\u201314020. https:\/\/doi.org\/10.1007\/s00500-022-07445-6","journal-title":"Soft Comput"},{"key":"10937_CR108","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2021.102715","volume":"213","author":"A Ali","year":"2022","unstructured":"Ali A, Gravino C (2022) Evaluating the impact of feature selection consistency in software prediction. Sci Comput Program 213:102715. https:\/\/doi.org\/10.1016\/j.scico.2021.102715","journal-title":"Sci Comput Program"},{"key":"10937_CR109","doi-asserted-by":"publisher","unstructured":"Khatri Y, Singh SK Search-based feature selection for cross-project fault prediction. In: 2022 IEEE Pune Section International Conference (PuneCon), pp. 1\u20135. https:\/\/doi.org\/10.1109\/PuneCon55413.2022.10014936","DOI":"10.1109\/PuneCon55413.2022.10014936"},{"issue":"S5","key":"10937_CR110","doi-asserted-by":"publisher","first-page":"10925","DOI":"10.1007\/s10586-017-1235-3","volume":"22","author":"M Anbu","year":"2019","unstructured":"Anbu M, Anandha Mala GS (2019) Feature selection using firefly algorithm in software defect prediction. Cluster Comput J Netw Software Tools Appl 22(S5):10925\u201310934. https:\/\/doi.org\/10.1007\/s10586-017-1235-3","journal-title":"Cluster Comput J Netw Software Tools Appl"},{"key":"10937_CR111","doi-asserted-by":"crossref","unstructured":"Mumtaz B, Kanwal S, Alamri S, Khan F (2021) Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction. Intelligent Automation & Soft Computing 29(3)","DOI":"10.32604\/iasc.2021.018405"},{"key":"10937_CR112","doi-asserted-by":"publisher","first-page":"14239","DOI":"10.1109\/ACCESS.2021.3052149","volume":"9","author":"Y Hassouneh","year":"2021","unstructured":"Hassouneh Y, Turabieh H, Thaher T, Tumar I, Chantar H, Too J (2021) Boosted whale optimization algorithm with natural selection operators for software fault prediction. IEEE Access 9:14239\u201314258. https:\/\/doi.org\/10.1109\/ACCESS.2021.3052149","journal-title":"IEEE Access"},{"issue":"25","key":"10937_CR113","doi-asserted-by":"publisher","first-page":"7240","DOI":"10.1002\/cpe.7240","volume":"34","author":"M Anbu","year":"2022","unstructured":"Anbu M (2022) Improved mayfly optimization deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout XLM learning framework for software defect prediction. Concurr Comput Practice Exp 34(25):7240. https:\/\/doi.org\/10.1002\/cpe.7240","journal-title":"Concurr Comput Practice Exp"},{"key":"10937_CR114","doi-asserted-by":"publisher","first-page":"1028175","DOI":"10.1155\/2022\/1028175","volume":"2022","author":"UG Mohammad","year":"2022","unstructured":"Mohammad UG, Imtiaz S, Shakya M, Almadhor A, Anwar F (2022) An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems. Wirel Commun Mob Comput 2022:1028175. https:\/\/doi.org\/10.1155\/2022\/1028175","journal-title":"Wirel Commun Mob Comput"},{"key":"10937_CR115","doi-asserted-by":"publisher","unstructured":"Xu Z, Liu J, Yang Z, An G, Jia X The impact of feature selection on defect prediction performance: An empirical comparison. In: 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), pp. 309\u2013320. https:\/\/doi.org\/10.1109\/ISSRE.2016.13","DOI":"10.1109\/ISSRE.2016.13"},{"key":"10937_CR116","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1109\/ACCESS.2017.2785445","volume":"6","author":"S Huda","year":"2018","unstructured":"Huda S, Alyahya S, Mohsin Ali M, Ahmad S, Abawajy J, Al-Dossari H, Yearwood J (2018) A framework for software defect prediction and metric selection. IEEE Access 6:2844\u20132858. https:\/\/doi.org\/10.1109\/ACCESS.2017.2785445","journal-title":"IEEE Access"},{"key":"10937_CR117","doi-asserted-by":"publisher","first-page":"5069016","DOI":"10.1155\/2021\/5069016","volume":"2021","author":"AO Balogun","year":"2021","unstructured":"Balogun AO, Basri S, Mahamad S, Capretz LF, Imam AA, Almomani MA, Adeyemo VE, Kumar G (2021) A novel rank aggregation-based hybrid multifilter wrapper feature selection method in software defect prediction. Comput Intell Neurosci 2021:5069016. https:\/\/doi.org\/10.1155\/2021\/5069016","journal-title":"Comput Intell Neurosci"},{"issue":"4","key":"10937_CR118","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.1007\/s40747-022-00676-y","volume":"8","author":"L Chen","year":"2022","unstructured":"Chen L, Wang C, Song S (2022) Software defect prediction based on nested-stacking and heterogeneous feature selection. Complex Intell Syst 8(4):3333\u20133348. https:\/\/doi.org\/10.1007\/s40747-022-00676-y","journal-title":"Complex Intell Syst"},{"issue":"6","key":"10937_CR119","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1007\/s11390-017-1785-0","volume":"32","author":"C Ni","year":"2017","unstructured":"Ni C, Liu W-S, Chen X, Gu Q, Chen D-X, Huang Q-G (2017) A cluster based feature selection method for cross-project software defect prediction. J Comput Sci Technol 32(6):1090\u20131107. https:\/\/doi.org\/10.1007\/s11390-017-1785-0","journal-title":"J Comput Sci Technol"},{"key":"10937_CR120","doi-asserted-by":"publisher","unstructured":"Jian Y, Yu X, Xu Z, Ma Z (2019) A hybrid feature selection method for software fault prediction. IEICE Trans Inf Syst E102.D(10):1966\u20131975. https:\/\/doi.org\/10.1587\/transinf.2019EDP7033","DOI":"10.1587\/transinf.2019EDP7033"},{"key":"10937_CR121","doi-asserted-by":"publisher","unstructured":"Xu X, Chen W, Wang X (2021) RFC: a feature selection algorithm for software defect prediction. J Syst Eng Electron 32(2):389\u2013398. https:\/\/doi.org\/10.23919\/JSEE.2021.000032","DOI":"10.23919\/JSEE.2021.000032"},{"issue":"2","key":"10937_CR122","doi-asserted-by":"publisher","first-page":"515","DOI":"10.2298\/CSIS180312039B","volume":"16","author":"E Borandag","year":"2019","unstructured":"Borandag E, Ozcift A, Kilinc D, Yucalar F (2019) Majority vote feature selection algorithm in software fault prediction. Comput Sci Inf Syst 16(2):515\u2013539. https:\/\/doi.org\/10.2298\/CSIS180312039B","journal-title":"Comput Sci Inf Syst"},{"issue":"7","key":"10937_CR123","doi-asserted-by":"publisher","first-page":"13044","DOI":"10.1111\/exsy.13044","volume":"39","author":"P Yildirim Taser","year":"2022","unstructured":"Yildirim Taser P (2022) A novel multi-view ordinal classification approach for software bug prediction. Expert Syst 39(7):13044. https:\/\/doi.org\/10.1111\/exsy.13044","journal-title":"Expert Syst"},{"key":"10937_CR124","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109737","volume":"131","author":"KJ Thirumoorthy","year":"2022","unstructured":"Thirumoorthy KJ (2022) A feature selection model for software defect prediction using binary Rao optimization algorithm. Appl Soft Comput 131:109737. https:\/\/doi.org\/10.1016\/j.asoc.2022.109737","journal-title":"Appl Soft Comput"},{"key":"10937_CR125","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2013.04.027","volume":"241","author":"NJ Pizzi","year":"2013","unstructured":"Pizzi NJ (2013) A fuzzy classifier approach to estimating software quality. Inf Sci 241:1\u201311. https:\/\/doi.org\/10.1016\/j.ins.2013.04.027","journal-title":"Inf Sci"},{"key":"10937_CR126","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.infsof.2015.03.001","volume":"63","author":"HB Yadav","year":"2015","unstructured":"Yadav HB, Yadav DK (2015) A fuzzy logic based approach for phase-wise software defects prediction using software metrics. Inf Softw Technol 63:44\u201357. https:\/\/doi.org\/10.1016\/j.infsof.2015.03.001","journal-title":"Inf Softw Technol"},{"issue":"1","key":"10937_CR127","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10586-018-1923-7","volume":"22","author":"C Viji","year":"2019","unstructured":"Viji C, Rajkumar N, Duraisamy S (2019) Prediction of software fault-prone classes using an unsupervised hybrid SOM algorithm. Cluster Comput J Netw Software Tools Appl 22(1):133\u2013143. https:\/\/doi.org\/10.1007\/s10586-018-1923-7","journal-title":"Cluster Comput J Netw Software Tools Appl"},{"key":"10937_CR128","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1016\/j.asoc.2019.02.008","volume":"77","author":"K Juneja","year":"2019","unstructured":"Juneja K (2019) A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation. Appl Soft Comput 77:696\u2013713. https:\/\/doi.org\/10.1016\/j.asoc.2019.02.008","journal-title":"Appl Soft Comput"},{"issue":"2","key":"10937_CR129","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1109\/TR.2014.2316951","volume":"63","author":"Liu Mx","year":"2014","unstructured":"Mx Liu, Miao L, Zhang D (2014) Two-stage cost-sensitive learning for software defect prediction. IEEE Trans Reliab 63(2):676\u2013686. https:\/\/doi.org\/10.1109\/TR.2014.2316951","journal-title":"IEEE Trans Reliab"},{"issue":"11","key":"10937_CR130","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1631\/FITEE.1601322","volume":"18","author":"Q Yu","year":"2017","unstructured":"Yu Q, Jiang S, Wang R, Wang H (2017) A feature selection approach based on a similarity measure for software defect prediction. Front Inf Technol Electronic Eng 18(11):1744\u20131753","journal-title":"Front Inf Technol Electronic Eng"},{"issue":"1","key":"10937_CR131","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/TR.2018.2864206","volume":"68","author":"SS Rathore","year":"2019","unstructured":"Rathore SS, Kumar S (2019) An approach for the prediction of number of software faults based on the dynamic selection of learning techniques. IEEE Trans Reliab 68(1):216\u2013236. https:\/\/doi.org\/10.1109\/TR.2018.2864206","journal-title":"IEEE Trans Reliab"},{"issue":"11","key":"10937_CR132","doi-asserted-by":"publisher","first-page":"2689","DOI":"10.1109\/TNNLS.2015.2391171","volume":"26","author":"SP Chatzis","year":"2015","unstructured":"Chatzis SP, Andreou AS (2015) Maximum entropy discrimination poisson regression for software reliability modeling. IEEE Trans Neural Netw Learn Syst 26(11):2689\u20132701. https:\/\/doi.org\/10.1109\/TNNLS.2015.2391171","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"10937_CR133","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/TSE.2016.2599161","volume":"43","author":"F Zhang","year":"2017","unstructured":"Zhang F, Hassan AE, McIntosh S, Zou Y (2017) The use of summation to aggregate software metrics hinders the performance of defect prediction models. IEEE Trans Software Eng 43(5):476\u2013491. https:\/\/doi.org\/10.1109\/TSE.2016.2599161","journal-title":"IEEE Trans Software Eng"},{"key":"10937_CR134","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.infsof.2017.07.004","volume":"92","author":"MM \u00d6zt\u00fcrk","year":"2017","unstructured":"\u00d6zt\u00fcrk MM (2017) Which type of metrics are useful to deal with class imbalance in software defect prediction? Inf Softw Technol 92:17\u201329. https:\/\/doi.org\/10.1016\/j.infsof.2017.07.004","journal-title":"Inf Softw Technol"},{"issue":"S1","key":"10937_CR135","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10586-018-1730-1","volume":"22","author":"R Jayanthi","year":"2019","unstructured":"Jayanthi R, Florence L (2019) Software defect prediction techniques using metrics based on neural network classifier. Cluster Comput J Netw Software Tools Appl 22(S1):77\u201388. https:\/\/doi.org\/10.1007\/s10586-018-1730-1","journal-title":"Cluster Comput J Netw Software Tools Appl"},{"issue":"16","key":"10937_CR136","doi-asserted-by":"publisher","first-page":"10551","DOI":"10.1007\/s00521-021-05811-3","volume":"33","author":"S Mehta","year":"2021","unstructured":"Mehta S, Patnaik KS (2021) Improved prediction of software defects using ensemble machine learning techniques. Neural Comput Appl 33(16):10551\u201310562. https:\/\/doi.org\/10.1007\/s00521-021-05811-3","journal-title":"Neural Comput Appl"},{"key":"10937_CR137","doi-asserted-by":"publisher","unstructured":"Ho A, Nhat\u00a0Hai N, Thi-Mai-Anh B Combining deep learning and kernel PCA for software defect prediction. In: Proceedings of the 11th International Symposium on Information and Communication Technology. SoICT \u201922, pp. 360\u2013367. Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3568562.3568587","DOI":"10.1145\/3568562.3568587"},{"issue":"4","key":"10937_CR138","doi-asserted-by":"publisher","first-page":"9847","DOI":"10.1007\/s10586-018-1696-z","volume":"22","author":"C Manjula","year":"2019","unstructured":"Manjula C, Florence L (2019) Deep neural network based hybrid approach for software defect prediction using software metrics. Cluster Comput J Netw Software Tools Appl 22(4):9847\u20139863. https:\/\/doi.org\/10.1007\/s10586-018-1696-z","journal-title":"Cluster Comput J Netw Software Tools Appl"},{"issue":"2","key":"10937_CR139","first-page":"1467","volume":"65","author":"K Zhu","year":"2020","unstructured":"Zhu K, Zhang N, Zhang Q, Ying S, Wang X (2020) Software defect prediction based on non-linear manifold learning and hybrid deep learning techniques. Cmc-Comput Mater Contin 65(2):1467\u20131486","journal-title":"Cmc-Comput Mater Contin"},{"issue":"1","key":"10937_CR140","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1049\/sfw2.12029","volume":"16","author":"N Zhang","year":"2022","unstructured":"Zhang N, Ying S, Zhu K, Zhu D (2022) Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine. IET Software 16(1):29\u201347. https:\/\/doi.org\/10.1049\/sfw2.12029","journal-title":"IET Software"},{"key":"10937_CR141","doi-asserted-by":"publisher","unstructured":"Zhu K, Zhang N, Ying S, Zhu D Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network 14(3), 185\u2013195 https:\/\/doi.org\/10.1049\/iet-sen.2019.0278. Accessed 2024-03-05","DOI":"10.1049\/iet-sen.2019.0278"},{"key":"10937_CR142","doi-asserted-by":"publisher","unstructured":"Zou Q, Lu L, Yang Z, Gu X, Qiu S Joint feature representation learning and progressive distribution matching for cross-project defect prediction 137, 106588 https:\/\/doi.org\/10.1016\/j.infsof.2021.106588","DOI":"10.1016\/j.infsof.2021.106588"},{"issue":"5","key":"10937_CR143","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.3390\/app10051892","volume":"10","author":"J Ren","year":"2020","unstructured":"Ren J, Liu F (2020) A novel approach for software defect prediction based on the power law function. Appl Sci-Basel 10(5):1892. https:\/\/doi.org\/10.3390\/app10051892","journal-title":"Appl Sci-Basel"},{"issue":"9","key":"10937_CR144","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TSE.2007.70721","volume":"33","author":"T Menzies","year":"2007","unstructured":"Menzies T, Dekhtyar A, Distefano J, Greenwald J (2007) Problems with precision: a response to \u201ccomments on \u2018data mining static code attributes to learn defect predictors\u201d\u2019. IEEE Trans Software Eng 33(9):637\u2013640. https:\/\/doi.org\/10.1109\/TSE.2007.70721","journal-title":"IEEE Trans Software Eng"},{"issue":"5","key":"10937_CR145","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/s10664-021-09984-2","volume":"26","author":"M Ulan","year":"2021","unstructured":"Ulan M, L\u00f6we W, Ericsson M, Wingkvist A (2021) Weighted software metrics aggregation and its application to defect prediction. Empir Softw Eng 26(5):86. https:\/\/doi.org\/10.1007\/s10664-021-09984-2","journal-title":"Empir Softw Eng"},{"key":"10937_CR146","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-018-2326-5","author":"A Kalsoom","year":"2018","unstructured":"Kalsoom A, Maqsood M, Ghazanfar Ma, Aadil F, Rho S (2018) A dimensionality reduction-based efficient software fault prediction using Fisher linear discriminant analysis (FLDA). J Supercomput. https:\/\/doi.org\/10.1007\/s11227-018-2326-5","journal-title":"J Supercomput"},{"key":"10937_CR147","doi-asserted-by":"publisher","first-page":"47047","DOI":"10.1109\/ACCESS.2018.2866082","volume":"6","author":"A Arshad","year":"2018","unstructured":"Arshad A, Riaz S, Jiao L, Murthy A (2018) The empirical study of semi-supervised deep fuzzy c-mean clustering for software fault prediction. IEEE Access 6:47047\u201347061. https:\/\/doi.org\/10.1109\/ACCESS.2018.2866082","journal-title":"IEEE Access"},{"key":"10937_CR148","doi-asserted-by":"publisher","unstructured":"Sun J, Ji Y, Liu S, Wu F (2020) Cost-sensitive and sparse ladder network for software defect prediction. IEICE Trans Inf Syst E103.D(5):1177\u20131180. https:\/\/doi.org\/10.1587\/transinf.2019EDL8198","DOI":"10.1587\/transinf.2019EDL8198"},{"key":"10937_CR149","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.neucom.2021.05.043","volume":"460","author":"NS Harzevili","year":"2021","unstructured":"Harzevili NS, Alizadeh SH (2021) Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables. Neurocomputing 460:309\u2013330. https:\/\/doi.org\/10.1016\/j.neucom.2021.05.043","journal-title":"Neurocomputing"},{"issue":"4","key":"10937_CR150","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1049\/iet-sen.2019.0149","volume":"14","author":"J Deng","year":"2020","unstructured":"Deng J, Lu L, Qiu S (2020) Software defect prediction via LSTM. IET Software 14(4):443\u2013450. https:\/\/doi.org\/10.1049\/iet-sen.2019.0149","journal-title":"IET Software"},{"key":"10937_CR151","doi-asserted-by":"publisher","first-page":"4311548","DOI":"10.1155\/2022\/4311548","volume":"2022","author":"F Yang","year":"2022","unstructured":"Yang F, Huang Y, Xu H, Xiao P, Zheng W (2022) Fine-grained software defect prediction based on the method-call sequence. Comput Intell Neurosci 2022:4311548. https:\/\/doi.org\/10.1155\/2022\/4311548","journal-title":"Comput Intell Neurosci"},{"issue":"5","key":"10937_CR152","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1109\/TCSS.2020.3017501","volume":"7","author":"I Alazzam","year":"2020","unstructured":"Alazzam I, Aleroud A, Al Latifah Z, Karabatis G (2020) Automatic bug triage in software systems using graph neighborhood relations for feature augmentation. IEEE Trans Comput Soc Syst 7(5):1288\u20131303. https:\/\/doi.org\/10.1109\/TCSS.2020.3017501","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"10937_CR153","doi-asserted-by":"publisher","unstructured":"Rahman F, Devanbu P How, and why, process metrics are better. In: 2013 35th International Conference on Software Engineering (ICSE), pp. 432\u2013441. https:\/\/doi.org\/10.1109\/ICSE.2013.6606589","DOI":"10.1109\/ICSE.2013.6606589"},{"issue":"1","key":"10937_CR154","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s10664-012-9218-8","volume":"19","author":"A Okutan","year":"2014","unstructured":"Okutan A, Y\u0131ld\u0131z OT (2014) Software defect prediction using bayesian networks. Empir Softw Eng 19(1):154\u2013181. https:\/\/doi.org\/10.1007\/s10664-012-9218-8","journal-title":"Empir Softw Eng"},{"key":"10937_CR155","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.jss.2014.01.033","volume":"93","author":"C Couto","year":"2014","unstructured":"Couto C, Pires P, Valente MT, Bigonha RS, Anquetil N (2014) Predicting software defects with causality tests. J Syst Softw 93:24\u201341","journal-title":"J Syst Softw"},{"key":"10937_CR156","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.eswa.2016.05.018","volume":"61","author":"\u00d6F Arar","year":"2016","unstructured":"Arar \u00d6F, Ayan K (2016) Deriving thresholds of software metrics to predict faults on open source software: Replicated case studies. Expert Syst Appl 61:106\u2013121. https:\/\/doi.org\/10.1016\/j.eswa.2016.05.018","journal-title":"Expert Syst Appl"},{"key":"10937_CR157","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.csi.2017.02.003","volume":"53","author":"L Kumar","year":"2017","unstructured":"Kumar L, Misra S, Rath SK (2017) An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes. Comput Standards Interfaces 53:1\u201332","journal-title":"Comput Standards Interfaces"},{"key":"10937_CR158","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.compeleceng.2018.02.043","volume":"67","author":"GR Choudhary","year":"2018","unstructured":"Choudhary GR, Kumar S, Kumar K, Mishra A, Catal C (2018) Empirical analysis of change metrics for software fault prediction. Comput Electri Eng 67:15\u201324","journal-title":"Comput Electri Eng"},{"issue":"4","key":"10937_CR159","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1016\/j.aej.2018.01.003","volume":"57","author":"S Moustafa","year":"2018","unstructured":"Moustafa S, ElNainay MY, Makky NE, Abougabal MS (2018) Software bug prediction using weighted majority voting techniques. Alex Eng J 57(4):2763\u20132774. https:\/\/doi.org\/10.1016\/j.aej.2018.01.003","journal-title":"Alex Eng J"},{"key":"10937_CR160","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.infsof.2018.10.003","volume":"106","author":"X Chen","year":"2019","unstructured":"Chen X, Zhang D, Zhao Y, Cui Z, Ni C (2019) Software defect number prediction: Unsupervised vs supervised methods. Inf Softw Technol 106:161\u2013181. https:\/\/doi.org\/10.1016\/j.infsof.2018.10.003","journal-title":"Inf Softw Technol"},{"key":"10937_CR161","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.infsof.2019.08.005","volume":"115","author":"MAS Bigonha","year":"2019","unstructured":"Bigonha MAS, Ferreira K, Souza P, Sousa B, Janu\u00e1rio M, Lima D (2019) The usefulness of software metric thresholds for detection of bad smells and fault prediction. Inf Softw Technol 115:79\u201392. https:\/\/doi.org\/10.1016\/j.infsof.2019.08.005","journal-title":"Inf Softw Technol"},{"key":"10937_CR162","unstructured":"Fil\u00f3 TGS, Bigonha MAS, Ferreira KAM (2015) A Catalogue of Thresholds for Object-Oriented Software Metrics, pp. 48\u201355"},{"key":"10937_CR163","doi-asserted-by":"publisher","first-page":"85262","DOI":"10.1109\/ACCESS.2019.2924040","volume":"7","author":"SR Aziz","year":"2019","unstructured":"Aziz SR, Khan T, Nadeem A (2019) Experimental validation of inheritance metrics\u2019 impact on software fault prediction. IEEE Access 7:85262\u201385275. https:\/\/doi.org\/10.1109\/ACCESS.2019.2924040","journal-title":"IEEE Access"},{"issue":"1","key":"10937_CR164","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s10115-018-1241-7","volume":"60","author":"MK Ndenga","year":"2019","unstructured":"Ndenga MK, Ganchev I, Mehat J, Wabwoba F, Akdag H (2019) Performance and cost-effectiveness of change burst metrics in predicting software faults. Knowl Inf Syst 60(1):275\u2013302. https:\/\/doi.org\/10.1007\/s10115-018-1241-7","journal-title":"Knowl Inf Syst"},{"key":"10937_CR165","doi-asserted-by":"publisher","unstructured":"Mohamed FA, Salama CR, Yousef AH, Salem AM A universal model for defective classes prediction using different object-oriented metrics suites. In: 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), pp. 65\u201370. https:\/\/doi.org\/10.1109\/NILES50944.2020.9257892","DOI":"10.1109\/NILES50944.2020.9257892"},{"issue":"3\u20134","key":"10937_CR166","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s10515-020-00277-4","volume":"27","author":"G Esteves","year":"2020","unstructured":"Esteves G, Figueiredo E, Veloso A, Viggiato M, Ziviani N (2020) Understanding machine learning software defect predictions. Autom Softw Eng 27(3\u20134):369\u2013392. https:\/\/doi.org\/10.1007\/s10515-020-00277-4","journal-title":"Autom Softw Eng"},{"key":"10937_CR167","doi-asserted-by":"publisher","first-page":"563","DOI":"10.7717\/peerj-cs.563","volume":"7","author":"SR Aziz","year":"2021","unstructured":"Aziz SR, Khan TA, Nadeem A (2021) Exclusive use and evaluation of inheritance metrics viability in software fault prediction-an experimental study. PeerJ Comput Sci 7:563. https:\/\/doi.org\/10.7717\/peerj-cs.563","journal-title":"PeerJ Comput Sci"},{"issue":"08","key":"10937_CR168","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1142\/S0218194021500364","volume":"31","author":"L Chen","year":"2021","unstructured":"Chen L, Song S, Wang C (2021) A novel effort measure method for effort-aware just-in-time software defect prediction. Int J Software Eng Knowl Eng 31(08):1145\u20131169. https:\/\/doi.org\/10.1142\/S0218194021500364","journal-title":"Int J Software Eng Knowl Eng"},{"issue":"1","key":"10937_CR169","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TR.2022.3149658","volume":"71","author":"S Yang","year":"2022","unstructured":"Yang S, Gou X, Yang M, Shao Q, Bian C, Jiang M, Qiao Y (2022) Software bug number prediction based on complex network theory and panel data model. IEEE Trans Reliab 71(1):162\u2013177. https:\/\/doi.org\/10.1109\/TR.2022.3149658","journal-title":"IEEE Trans Reliab"},{"issue":"5","key":"10937_CR170","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s10664-022-10147-0","volume":"27","author":"V Walunj","year":"2022","unstructured":"Walunj V, Gharibi G, Alanazi R, Lee Y (2022) Defect prediction using deep learning with Network Portrait Divergence for software evolution. Empir Softw Eng 27(5):118. https:\/\/doi.org\/10.1007\/s10664-022-10147-0","journal-title":"Empir Softw Eng"},{"key":"10937_CR171","doi-asserted-by":"publisher","unstructured":"Majumder S, Mody P, Menzies T Revisiting process versus product metrics: a large scale analysis 27(3), 60 https:\/\/doi.org\/10.1007\/s10664-021-10068-4. Accessed 2024-03-04","DOI":"10.1007\/s10664-021-10068-4"},{"key":"10937_CR172","doi-asserted-by":"publisher","first-page":"64801","DOI":"10.1109\/ACCESS.2022.3181995","volume":"10","author":"D-L Miholca","year":"2022","unstructured":"Miholca D-L, Tomescu V-I, Czibula G (2022) An in-depth analysis of the software features\u2019 impact on the performance of deep learning-based software defect predictors. IEEE Access 10:64801\u201364818. https:\/\/doi.org\/10.1109\/ACCESS.2022.3181995","journal-title":"IEEE Access"},{"key":"10937_CR173","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Xing, E.P., Jebara, T. (eds.) Proceedings of the 31st International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 32, pp. 1188\u20131196. PMLR, Bejing, China"},{"issue":"6","key":"10937_CR174","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9","volume":"41","author":"S Deerwester","year":"1990","unstructured":"Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391\u2013407","journal-title":"J Am Soc Inf Sci"},{"key":"10937_CR175","doi-asserted-by":"publisher","unstructured":"Zhang W, Du Y, Yoshida T, Wang Q, Li X SamEn-SVR: using sample entropy and support vector regression for bug number prediction 12(3), 183\u2013189 https:\/\/doi.org\/10.1049\/iet-sen.2017.0168. Accessed 2024-03-05","DOI":"10.1049\/iet-sen.2017.0168"},{"key":"10937_CR176","doi-asserted-by":"publisher","unstructured":"Wang J, Zhang C Software reliability prediction using a deep learning model based on the RNN encoder-decoder 170, 73\u201382 https:\/\/doi.org\/10.1016\/j.ress.2017.10.019. Accessed 2024-02-23","DOI":"10.1016\/j.ress.2017.10.019"},{"key":"10937_CR177","doi-asserted-by":"publisher","unstructured":"Qiao L, Li X, Umer Q, Guo P Deep learning based software defect prediction 385, 100\u2013110 https:\/\/doi.org\/10.1016\/j.neucom.2019.11.067. Accessed 2024-02-23","DOI":"10.1016\/j.neucom.2019.11.067"},{"key":"10937_CR178","doi-asserted-by":"publisher","unstructured":"Pandey SK, Tripathi AK DNNAttention: A deep neural network and attention based architecture for cross project defect number prediction 233, 107541 https:\/\/doi.org\/10.1016\/j.knosys.2021.107541. Accessed 2023-09-26","DOI":"10.1016\/j.knosys.2021.107541"},{"key":"10937_CR179","doi-asserted-by":"publisher","unstructured":"Alghanim F, Azzeh M, El-Hassan A, Qattous H Software defect density prediction using deep learning 10, 114629\u2013114641 https:\/\/doi.org\/10.1109\/ACCESS.2022.3217480","DOI":"10.1109\/ACCESS.2022.3217480"},{"issue":"1","key":"10937_CR180","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.jss.2009.06.055","volume":"83","author":"E Arisholm","year":"2010","unstructured":"Arisholm E, Briand LC, Johannessen EB (2010) A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. J Syst Softw 83(1):2\u201317. https:\/\/doi.org\/10.1016\/j.jss.2009.06.055","journal-title":"J Syst Softw"},{"issue":"6","key":"10937_CR181","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/TSE.2010.81","volume":"37","author":"Y Shin","year":"2011","unstructured":"Shin Y, Meneely A, Williams L, Osborne JA (2011) Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities. IEEE Trans Software Eng 37(6):772\u2013787. https:\/\/doi.org\/10.1109\/TSE.2010.81","journal-title":"IEEE Trans Software Eng"},{"key":"10937_CR182","doi-asserted-by":"publisher","unstructured":"Yao J, Shepperd M The impact of using biased performance metrics on software defect prediction research 139, 106664 https:\/\/doi.org\/10.1016\/j.infsof.2021.106664. Accessed 2024-02-23","DOI":"10.1016\/j.infsof.2021.106664"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10937-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10937-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10937-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T22:02:10Z","timestamp":1738792930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10937-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":182,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10937"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10937-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]},"assertion":[{"value":"13 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}