{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:18:29Z","timestamp":1772608709226,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"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":["Empir Software Eng"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10664-023-10439-z","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T12:02:51Z","timestamp":1708689771000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction"],"prefix":"10.1007","volume":"29","author":[{"given":"Fengyu","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7364-4331","authenticated-orcid":false,"given":"Fa","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Guangdong","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"10439_CR1","doi-asserted-by":"publisher","unstructured":"Abdous S, Abdollahzadeh R, Rohban M H (2023) KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images[J]. arXiv preprint arXiv:2304.08240. https:\/\/doi.org\/10.48550\/arXiv.2304.08240","DOI":"10.48550\/arXiv.2304.08240"},{"key":"10439_CR2","doi-asserted-by":"publisher","unstructured":"Aftandilian E, Sauciuc R, Priya S et al (2012) Building Useful Program Analysis Tools using An Extensible Java Compiler[C]\/\/2012 IEEE 12th International Working Conference on Source Code Analysis and Manipulation. IEEE:14\u201323. https:\/\/doi.org\/10.1109\/SCAM.2012.28","DOI":"10.1109\/SCAM.2012.28"},{"key":"10439_CR3","doi-asserted-by":"publisher","unstructured":"Allamanis M, Brockschmidt M, Khademi M (2017) Learning to Represent Programs with Graphs[J]. arXiv preprint arXiv:1711.00740. https:\/\/doi.org\/10.48550\/arXiv.1711.00740","DOI":"10.48550\/arXiv.1711.00740"},{"key":"10439_CR4","doi-asserted-by":"publisher","unstructured":"Cao S, Sun X, Bo L, et al. (2022) MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks[J]. arXiv preprint arXiv:2203.02660. https:\/\/doi.org\/10.1145\/3510003.3510219","DOI":"10.1145\/3510003.3510219"},{"key":"10439_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: Synthetic Minority Over-sampling Technique[J]. J Artif Intell Res 16:321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artif Intell Res"},{"issue":"12","key":"10439_CR6","doi-asserted-by":"publisher","first-page":"3694","DOI":"10.13328\/j.cnki.jos.005604","volume":"30","author":"X Chen","year":"2019","unstructured":"Chen X, Zhao YQ, Gu Q, Ni C, Wang Z (2019) Empirical Studies on Multi-objective File-level Software Defect Prediction Method. Ruan Jian Xue Bao\/J Software 30(12):3694\u20133713 (in Chinese). https:\/\/doi.org\/10.13328\/j.cnki.jos.005604","journal-title":"Ruan Jian Xue Bao\/J Software"},{"issue":"3","key":"10439_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3436877","volume":"30","author":"X Cheng","year":"2021","unstructured":"Cheng X, Wang H, Hua J et al (2021) Deepwukong: Statically Detecting Software Vulnerabilities using Deep Graph Neural Network[J]. ACM Trans Software Engin Methodology (TOSEM) 30(3):1\u201333. https:\/\/doi.org\/10.1145\/3436877","journal-title":"ACM Trans Software Engin Methodology (TOSEM)"},{"key":"10439_CR8","doi-asserted-by":"publisher","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, et al. (2014) Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation[J]. arXiv preprint arXiv:1406.1078. https:\/\/doi.org\/10.48550\/arXiv.1406.1078","DOI":"10.48550\/arXiv.1406.1078"},{"key":"10439_CR9","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587","volume-title":"Statistical Power Analysis for the Behavioral Sciences[M]","author":"J Cohen","year":"2013","unstructured":"Cohen J (2013) Statistical Power Analysis for the Behavioral Sciences[M]. Academic press"},{"issue":"1","key":"10439_CR10","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/TSE.2018.2881961","volume":"47","author":"HK Dam","year":"2018","unstructured":"Dam HK, Tran T, Pham T et al (2018) Automatic Feature Learning for Predicting Vulnerable Software Components[J]. IEEE Trans Softw Eng 47(1):67\u201385. https:\/\/doi.org\/10.1109\/TSE.2018.2881961","journal-title":"IEEE Trans Softw Eng"},{"key":"10439_CR11","doi-asserted-by":"publisher","unstructured":"Ferrante J , Ottenstein K J , Warren JD . The Program Dependence Graph and Its Use in Optimization[J]. International Symposium on Programming, 6th Colloquium, Toulouse, April 17-19, 1984, Proceedings, 1984. https:\/\/doi.org\/10.1145\/24039.24041","DOI":"10.1145\/24039.24041"},{"issue":"3","key":"10439_CR12","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/0378-8733(78)90021-7","volume":"1","author":"LC Freeman","year":"1978","unstructured":"Freeman LC (1978) Centrality in Social Networks Conceptual Clarification[J]. Soc Networks 1(3):215\u2013239. https:\/\/doi.org\/10.1016\/0378-8733(78)90021-7","journal-title":"Soc Networks"},{"issue":"2","key":"10439_CR13","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s10664-009-9117-9","volume":"15","author":"H Hata","year":"2010","unstructured":"Hata H, Mizuno O, Kikuno T (2010) Fault-prone Module Detection using Large-scale Text Features based on Spam Filtering[J]. Empir Softw Eng 15(2):147\u2013165. https:\/\/doi.org\/10.1007\/s10664-009-9117-9","journal-title":"Empir Softw Eng"},{"key":"10439_CR14","doi-asserted-by":"publisher","unstructured":"Hellendoorn VJ, Devanbu P (2017) Are Deep Neural Networks the Best Choice for Modeling Source Code?[C]\/\/Proceedings of the 2017 11th Joint Meeting on Foundations of. Softw Eng:763\u2013773. https:\/\/doi.org\/10.1145\/3106237.3106290","DOI":"10.1145\/3106237.3106290"},{"key":"10439_CR15","doi-asserted-by":"publisher","unstructured":"Hin D, Kan A, Chen H, et al. (2022) LineVD: Statement-level Vulnerability Detection using Graph Neural Networks[C]\/\/Proceedings of the 19th International Conference on Mining Software Repositories. 596-607. https:\/\/doi.org\/10.1145\/3524842.3527949","DOI":"10.1145\/3524842.3527949"},{"key":"10439_CR16","doi-asserted-by":"publisher","unstructured":"Hindle A, Godfrey MW, Holt RC (2008) Reading Beside the Lines: Indentation as A Proxy for Complexity Metric[C]\/\/2008 16th IEEE International Conference on Program Comprehension. IEEE:133\u2013142. https:\/\/doi.org\/10.1109\/ICPC.2008.13","DOI":"10.1109\/ICPC.2008.13"},{"key":"10439_CR17","doi-asserted-by":"publisher","unstructured":"Huang Q, Xia X, Lo D (2017) Supervised vs unsupervised Models: A Holistic Look at Effort-aware Just-in-time Defect Prediction[C]\/\/2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE:159\u2013170. https:\/\/doi.org\/10.1109\/ICSME.2017.51","DOI":"10.1109\/ICSME.2017.51"},{"issue":"2","key":"10439_CR18","first-page":"1","volume":"9","author":"S Ieee","year":"1994","unstructured":"Ieee S (1994) IEEE Standard Classification for Software Anomalies.[J]. IEEE Standard Indus 9(2):1\u20134","journal-title":"IEEE Standard Indus"},{"key":"10439_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2017.42","volume-title":"2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)","author":"L Jian","year":"2017","unstructured":"Jian L, He P, Zhu J et al (2017) Software Defect Prediction via Convolutional Neural Network[C]\/\/. In: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE. https:\/\/doi.org\/10.1109\/QRS.2017.42"},{"key":"10439_CR20","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/SANER.2016.56","volume":"2016","author":"Y Kamei","year":"2016","unstructured":"Kamei Y, Shihab E (2016) Defect Prediction: Accomplishments and Future Challenges. Leaders Tomorrow Symposium: Future Software Engineering FOSE@SANER 2016:33\u201345. https:\/\/doi.org\/10.1109\/SANER.2016.56","journal-title":"Leaders Tomorrow Symposium: Future Software Engineering FOSE@SANER"},{"issue":"1","key":"10439_CR21","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/BF02289026","volume":"18","author":"L Katz","year":"1953","unstructured":"Katz L (1953) A New Status Index Derived From Sociometric Analysis[J]. Psychometrika 18(1):39\u201343. https:\/\/doi.org\/10.1007\/BF02289026","journal-title":"Psychometrika"},{"key":"10439_CR22","doi-asserted-by":"publisher","unstructured":"Khakhar P, Dubey RK (2022) The Integrity of Machine Learning Algorithms Against Software Defect Prediction[M]\/\/Artificial Intelligence and Machine Learning for EDGE Computing. Academic Press:65\u201374. https:\/\/doi.org\/10.1016\/B978-0-12-824054-0.00027-7","DOI":"10.1016\/B978-0-12-824054-0.00027-7"},{"key":"10439_CR23","doi-asserted-by":"publisher","unstructured":"Kingma DP, Adam BJ (2014) A Method for Stochastic Optimization[J]. arXiv preprint arXiv:1412.6980. https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"10439_CR24","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1007\/s10664-019-09736-3","volume":"25","author":"M Kondo","year":"2020","unstructured":"Kondo M, German DM, Mizuno O et al (2020) The Impact of Context Metrics on Just-In-Time Defect Prediction[J]. Empir Softw Eng 25:890\u2013939. https:\/\/doi.org\/10.1007\/s10664-019-09736-3","journal-title":"Empir Softw Eng"},{"issue":"2","key":"10439_CR25","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/0020-0190(87)90102-5","volume":"24","author":"B Korel","year":"1987","unstructured":"Korel B (1987) The Program Dependence Graph in Static Program Testing[J]. Inf Process Lett 24(2):103\u2013108. https:\/\/doi.org\/10.1016\/0020-0190(87)90102-5","journal-title":"Inf Process Lett"},{"key":"10439_CR26","unstructured":"Le Q, Mikolov T (2014) Distributed Representations of Sentences and Documents[C]\/\/International conference on machine learning. PMLR:1188\u20131196 10.48550\/ arXiv.1405.4053"},{"issue":"11","key":"10439_CR27","doi-asserted-by":"publisher","first-page":"4008","DOI":"10.13328\/j.cnki.jos.006339","volume":"33","author":"XZ Li","year":"2022","unstructured":"Li XZ, Qing DJ, He YP, Ma HT (2022) Fine-grained Bug Location Method Based on Source Code Extension Information. Ruan Jian Xue Bao\/J Software 33(11):4008\u20134026 (in Chinese). https:\/\/doi.org\/10.13328\/j.cnki.jos.006339","journal-title":"Ruan Jian Xue Bao\/J Software"},{"key":"10439_CR28","doi-asserted-by":"publisher","unstructured":"Li Y, Wang S, Nguyen T N (2021) Vulnerability Detection with Fine-grained Interpretations[C]\/\/Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 292-303. https:\/\/doi.org\/10.1145\/3468264.3468597","DOI":"10.1145\/3468264.3468597"},{"key":"10439_CR29","doi-asserted-by":"publisher","unstructured":"Lou Y, Zhu Q, Dong J, et al. (2021) Boosting Coverage-based Fault Localization via Graph-based Representation Learning[C]\/\/Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 664-676. https:\/\/doi.org\/10.1145\/3468264.3468580","DOI":"10.1145\/3468264.3468580"},{"key":"10439_CR30","unstructured":"Lucic A, Ter Hoeve MA, Tolomei G et al (2022) Cf-gnnexplainer: Counterfactual Explanations for Graph Neural Networks[C]\/\/International Conference on Artificial Intelligence and Statistics. PMLR:4499\u20134511"},{"key":"10439_CR31","unstructured":"Lundberg S M, Lee S I (2017) A Unified Approach to Interpreting Model Predictions[J]. Advances in Neural Information Processing Systems, 30."},{"key":"10439_CR32","doi-asserted-by":"publisher","unstructured":"Luo D, Cheng W, Xu D, et al. (2020) Parameterized Explainer for Graph Geural Getwork[J]. Advances in Neural Information Processing Systems, 33: 19620-19631. https:\/\/doi.org\/10.48550\/arXiv.2011.04573","DOI":"10.48550\/arXiv.2011.04573"},{"key":"10439_CR33","doi-asserted-by":"publisher","unstructured":"Mileti\u0107 M, Vuku\u0161i\u0107 M, Mau\u0161a G, et al. Cross-release Code Churn Impact on Effort-aware Software Defect Prediction[C]\/\/2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) IEEE, 2018: 1460-1466. https:\/\/doi.org\/10.23919\/MIPRO.2018.8400263","DOI":"10.23919\/MIPRO.2018.8400263"},{"key":"10439_CR34","doi-asserted-by":"publisher","first-page":"3977","DOI":"10.1007\/s10664-020-09861-4","volume":"25","author":"S Morasca","year":"2020","unstructured":"Morasca S, Lavazza L (2020) On the Assessment of Software Defect Prediction Models via ROC Curves[J]. Empir Softw Eng 25:3977\u20134019. https:\/\/doi.org\/10.1007\/s10664-020-09861-4","journal-title":"Empir Softw Eng"},{"key":"10439_CR35","doi-asserted-by":"publisher","unstructured":"Nguyen HH et al (2022) MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities. In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), Shenzhen, China, pp 1\u201310. https:\/\/doi.org\/10.1109\/DSAA54385.2022.10032337","DOI":"10.1109\/DSAA54385.2022.10032337"},{"key":"10439_CR36","doi-asserted-by":"publisher","unstructured":"Parnin C, Orso A (2011) Are Automated Debugging Techniques Actually Helping Programmers?[C] \/\/Proceedings of the 2011 international symposium on software testing and analysis. 199-209. https:\/\/doi.org\/10.1145\/2001420.2001445","DOI":"10.1145\/2001420.2001445"},{"key":"10439_CR37","doi-asserted-by":"publisher","unstructured":"Pornprasit C, Tantithamthavorn C (2022) DeepLineDP: Towards A Deep Learning Approach for Line-Level Defect Prediction[J]. IEEE Trans Softw Eng. https:\/\/doi.org\/10.1109\/TSE.2022.3144348","DOI":"10.1109\/TSE.2022.3144348"},{"key":"10439_CR38","doi-asserted-by":"publisher","unstructured":"Pornprasit C, Tantithamthavorn CK (2021) JITLine: A Simpler, Better, Faster, Finer-grained Just-in-time Defect Prediction[C]\/\/2021 IEEE\/ACM 18th International Conference on Mining Software Repositories (MSR). IEEE:369\u2013379. https:\/\/doi.org\/10.1109\/MSR52588.2021.00049","DOI":"10.1109\/MSR52588.2021.00049"},{"issue":"OOPSLA","key":"10439_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3276517","volume":"2","author":"M Pradel","year":"2018","unstructured":"Pradel M, Sen K (2018) Deepbugs: A Learning Approach to Name-based Bug Detection[J]. Proc ACM Prog Lang 2(OOPSLA):1\u201325. https:\/\/doi.org\/10.1145\/3276517","journal-title":"Proc ACM Prog Lang"},{"key":"10439_CR40","doi-asserted-by":"publisher","unstructured":"Rahman F, Posnett D, Devanbu P (2012) Recalling the \u201cImprecision\u201d of Cross-project Defect Prediction[C]\/\/Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering. 1-11. https:\/\/doi.org\/10.1145\/2393596.2393669","DOI":"10.1145\/2393596.2393669"},{"key":"10439_CR41","doi-asserted-by":"publisher","unstructured":"Ray B, Hellendoorn V, Godhane S, et al. (2016) On the \u201cNaturalness\u201d of Buggy Code[C]\/ \/Proceedings of the 38th International Conference on Software Engineering. 428-439. https:\/\/doi.org\/10.1145\/2884781.2884848","DOI":"10.1145\/2884781.2884848"},{"key":"10439_CR42","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201cWhy Should I Trust You?\u201d Explaining the predictions of any classifier[C]\/\/Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135-1144. https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"3","key":"10439_CR43","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins H, Monro S (1951) A Stochastic Approximation Method[J]. Ann Math Stat 22(3):400\u2013407. https:\/\/doi.org\/10.1214\/aoms\/1177729586","journal-title":"Ann Math Stat"},{"key":"10439_CR44","doi-asserted-by":"publisher","unstructured":"Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling Relational Data with Graph Convolutional Networks[C]\/\/The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15. Springer International Publishing, 2018: 593-607. https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"10439_CR45","doi-asserted-by":"publisher","unstructured":"Sohn J, Kamei Y, McIntosh S et al (2021) Leveraging Fault Localisation to Enhance Defect Prediction[C]\/\/2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE:284\u2013294. https:\/\/doi.org\/10.1109\/SANER50967.2021.00034","DOI":"10.1109\/SANER50967.2021.00034"},{"key":"10439_CR46","doi-asserted-by":"crossref","unstructured":"Staniak M, Biecek P (2018) Explanations of Model Predictions with Live and BreakDown Packages[J]. arXiv preprint arXiv:1804.01955. 10.48550\/ arXiv.1804.01955","DOI":"10.32614\/RJ-2018-072"},{"key":"10439_CR47","doi-asserted-by":"publisher","unstructured":"Tang L, Tao C, Guo H et al (2022) Software Defect Prediction via GCN based on Structural and Context Information[C]\/\/2022 9th International Conference on Dependable Systems and Their Applications (DSA). IEEE:310\u2013319. https:\/\/doi.org\/10.1109\/DSA56465.2022.00049","DOI":"10.1109\/DSA56465.2022.00049"},{"issue":"11","key":"10439_CR48","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TSE.2018.2876537","volume":"46","author":"C Tantithamthavorn","year":"2018","unstructured":"Tantithamthavorn C, Hassan AE, Matsumoto K (2018b) The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models[J]. IEEE Trans Softw Eng 46(11):1200\u20131219. https:\/\/doi.org\/10.1109\/TSE.2018.2876537","journal-title":"IEEE Trans Softw Eng"},{"key":"10439_CR49","doi-asserted-by":"publisher","unstructured":"Tantithamthavorn C, McIntosh S, Hassan A E, et al. (2016a) Automated Parameter Optimization of Classification Techniques for Defect Prediction Models[C]\/\/Proceedings of the 38th International Conference on Software Engineering. 321-332. https:\/\/doi.org\/10.1145\/2884781.2884857","DOI":"10.1145\/2884781.2884857"},{"issue":"1","key":"10439_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TSE.2016.2584050","volume":"43","author":"C Tantithamthavorn","year":"2016","unstructured":"Tantithamthavorn C, McIntosh S, Hassan AE et al (2016b) An Empirical Comparison of Model Validation Techniques for Defect Prediction Models[J]. IEEE Trans Softw Eng 43(1):1\u201318. https:\/\/doi.org\/10.1109\/TSE.2016.2584050","journal-title":"IEEE Trans Softw Eng"},{"issue":"7","key":"10439_CR51","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/TSE.2018.2794977","volume":"45","author":"C Tantithamthavorn","year":"2018","unstructured":"Tantithamthavorn C, McIntosh S, Hassan AE et al (2018a) The Impact of Automated Parameter Optimization on Defect Prediction Models[J]. IEEE Trans Softw Eng 45(7):683\u2013711. https:\/\/doi.org\/10.1109\/TSE.2018.2794977","journal-title":"IEEE Trans Softw Eng"},{"issue":"04","key":"10439_CR52","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1109\/SANER.2016.56","volume":"13","author":"S Uddin","year":"2014","unstructured":"Uddin S, Hossain L, Wigand RT (2014) New Direction in Degree Centrality Measure: Towards a Time-variant Approach[J]. Int J Inf Technol Decis Mak 13(04):865\u2013878. https:\/\/doi.org\/10.1109\/SANER.2016.56","journal-title":"Int J Inf Technol Decis Mak"},{"key":"10439_CR53","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is All You Need[J]. Advances in neural information processing systems, 30. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"10439_CR54","doi-asserted-by":"publisher","unstructured":"Wan Z , Xia X , Hassan A E , et al. (2018a) Perceptions, Expectations, and Challenges in Defect Prediction[J]. IEEE Transactions on Software Engineering, PP:1-1. https:\/\/doi.org\/10.1109\/TSE.2018.2877678","DOI":"10.1109\/TSE.2018.2877678"},{"issue":"11","key":"10439_CR55","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1109\/TSE.2018.2877678","volume":"46","author":"Z Wan","year":"2018","unstructured":"Wan Z, Xia X, Hassan AE et al (2018b) Perceptions, Expectations, and Challenges in Defect Prediction[J]. IEEE Trans Softw Eng 46(11):1241\u20131266. https:\/\/doi.org\/10.1109\/TSE.2018.2877678","journal-title":"IEEE Trans Softw Eng"},{"key":"10439_CR56","doi-asserted-by":"publisher","unstructured":"Wang H, Khoshgoftaar TM, Napolitano A (2010) A comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction[C]\/\/2010 Ninth International Conference on Machine Learning and Applications. IEEE:135\u2013140. https:\/\/doi.org\/10.1109\/ICMLA.2010.27","DOI":"10.1109\/ICMLA.2010.27"},{"key":"10439_CR57","doi-asserted-by":"publisher","unstructured":"Wang S, Chollak D, Movshovitz-Attias D, et al. (2016) Bugram: Bug Detection with N-gram Language Models[C]\/\/Proceedings of the 31st IEEE\/ACM International Conference on Automated Software Engineering. 708-719. https:\/\/doi.org\/10.1145\/2970276.2970341","DOI":"10.1145\/2970276.2970341"},{"issue":"12","key":"10439_CR58","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TSE.2018.2877612","volume":"46","author":"S Wang","year":"2018","unstructured":"Wang S, Liu T, Nam J et al (2018) Deep Semantic Feature Learning for Software Defect Prediction[J]. IEEE Trans Softw Eng 46(12):1267\u20131293. https:\/\/doi.org\/10.1109\/TSE.2018.2877612","journal-title":"IEEE Trans Softw Eng"},{"key":"10439_CR59","doi-asserted-by":"publisher","unstructured":"Wang W, Li G, Ma B et al (2020) Detecting Code Clones with Graph Neural Network and Flow-augmented Abstract Syntax Tree[C]\/\/2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE:261\u2013271. https:\/\/doi.org\/10.1109\/SANER48275.2020.9054857","DOI":"10.1109\/SANER48275.2020.9054857"},{"key":"10439_CR60","doi-asserted-by":"publisher","unstructured":"Wang Y, Wang W, Joty S, et al. (2021) Codet5: Identifier-aware Unified Pre-trained Encoder-decoder Models for Code Understanding and Generation[J]. arXiv preprint arXiv:2109.00859. https:\/\/doi.org\/10.48550\/arXiv.2305.07922","DOI":"10.48550\/arXiv.2305.07922"},{"key":"10439_CR61","doi-asserted-by":"publisher","unstructured":"Wattanakriengkrai S, Thongtanunam P, Tantithamthavorn C et al (2020) Predicting Defective Lines Using a Model-Agnostic Technique[J]. IEEE Transac Software Eng. https:\/\/doi.org\/10.1109\/TSE.2020.3023177","DOI":"10.1109\/TSE.2020.3023177"},{"key":"10439_CR62","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2012.56","volume-title":"Investigating How Software was Responsible[C]\/\/ Fourth International Conference on Secure Software Integration & Reliability Improvement","author":"WE Wong","year":"2010","unstructured":"Wong WE, Debroy V, Surampudi A et al (2010) Recent Catastrophic Accidents. In: Investigating How Software was Responsible[C]\/\/ Fourth International Conference on Secure Software Integration & Reliability Improvement. IEEE Computer Society. https:\/\/doi.org\/10.1109\/TSE.2012.56"},{"key":"10439_CR63","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.jss.2017.06.069","volume":"133","author":"WE Wong","year":"2017","unstructured":"Wong WE, Li X, Laplante PA (2017) Be More Familiar with Our Enemies and Pave the Way Forward: A Review of the Roles Bugs Played in Software Failures[J]. J Syst Softw 133:68\u201394. https:\/\/doi.org\/10.1016\/j.jss.2017.06.069","journal-title":"J Syst Softw"},{"issue":"01","key":"10439_CR64","doi-asserted-by":"publisher","first-page":"35","DOI":"10.11897\/SP.J.1016.2022.00035","volume":"45","author":"B Wu","year":"2022","unstructured":"Wu B, Liang X, Zhang SS, Xu R (2022a) Advances and Applications in Graph Neural Network[J]. Chinese J Comput 45(01):35\u201368(in Chinese with English abstract). https:\/\/doi.org\/10.11897\/SP.J.1016.2022.00035","journal-title":"Chinese J Comput"},{"key":"10439_CR65","doi-asserted-by":"publisher","unstructured":"Wu Y, Zou D, Dou S, et al. (2022b) VulCNN: An Image-inspired Scalable Vulnerability Detection System[C]\/\/Proceedings of the 44th International Conference on Software Engineering. 2365-2376. https:\/\/doi.org\/10.1145\/3510003.3510229","DOI":"10.1145\/3510003.3510229"},{"issue":"1","key":"10439_CR66","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F et al (2020) A Comprehensive Survey on Graph Neural Networks[J]. IEEE Transac Neural Networks Learn Syst 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Transac Neural Networks Learn Syst"},{"key":"10439_CR67","doi-asserted-by":"publisher","unstructured":"Xiao Y, Jin L, Yang Z et al (2017) The Bayesian Network Based Program Dependence Graph and Its Application to Fault Localization[J]. J Syst Softw:134. https:\/\/doi.org\/10.1016\/j.jss.2017.08.025","DOI":"10.1016\/j.jss.2017.08.025"},{"key":"10439_CR68","doi-asserted-by":"publisher","unstructured":"Xu J, Ai J, Liu J et al (2022) ACGDP: An Augmented Code Graph-Based System for Software Defect Prediction[J]. IEEE Trans Reliab. https:\/\/doi.org\/10.1109\/TR.2022.3161581","DOI":"10.1109\/TR.2022.3161581"},{"issue":"1","key":"10439_CR69","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/TSE.2020.2978819","volume":"48","author":"M Yan","year":"2020","unstructured":"Yan M, Xia X, Fan Y et al (2020) Just-in-time Defect Identification and Localization: A two-phase Framework[J]. IEEE Trans Softw Eng 48(1):82\u2013101. https:\/\/doi.org\/10.1109\/TSE.2020.2978819","journal-title":"IEEE Trans Softw Eng"},{"key":"10439_CR70","doi-asserted-by":"crossref","unstructured":"Yang F Y, Zeng G D, Zhong F, et al. (2023) Interpretable Software Defect Prediction Incorporating Multiple Rules[C]\/\/2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE. 940-947.","DOI":"10.1109\/SANER56733.2023.00114"},{"key":"10439_CR71","doi-asserted-by":"publisher","unstructured":"Yang Z, Yang D, Dyer C, et al. (2016) Hierarchical Attention Networks for Document Classification[C]\/\/Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480-1489. https:\/\/doi.org\/10.18653\/v1\/N16-1174","DOI":"10.18653\/v1\/N16-1174"},{"key":"10439_CR72","doi-asserted-by":"publisher","unstructured":"Ying R , Bourgeois D , You J , et al. (2019) GNNExplainer: Generating Explanations for Graph Neural Networks[J]. Advances in Neural Information Processing Systems, 32:9240-9251. https:\/\/doi.org\/10.48550\/arXiv.1903.03894","DOI":"10.48550\/arXiv.1903.03894"},{"key":"10439_CR73","doi-asserted-by":"publisher","unstructured":"Zeng C, Zhou CY, Lv SK et al (2021) GCN2defect: Graph Convolutional Networks for SMOTETomek-based Software Defect Prediction[C]\/\/2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE). IEEE:69\u201379. https:\/\/doi.org\/10.1109\/ISSRE52982.2021.00020","DOI":"10.1109\/ISSRE52982.2021.00020"},{"key":"10439_CR74","doi-asserted-by":"publisher","unstructured":"Zhang Z, Lei Y, Yan M, et al. (2022) Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach[C]\/\/Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering. 1-13. https:\/\/doi.org\/10.1145\/3551349.3560428","DOI":"10.1145\/3551349.3560428"},{"key":"10439_CR75","doi-asserted-by":"crossref","unstructured":"Zheng W, Chen TF, Hu MT et al (2023) Hybrid Defect Prediction Model Based on Counterfactual Feature Optimization[J]. Human-Centric Intel Syst:1\u201315","DOI":"10.1007\/s44230-023-00034-2"},{"key":"10439_CR76","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.patrec.2020.03.030","volume":"136","author":"Q Zhu","year":"2020","unstructured":"Zhu Q (2020) On the Performance of Matthews Correlation Coefficient (MCC) for Imbalanced Dataset[J]. Pattern Recogn Lett 136:71\u201380. https:\/\/doi.org\/10.1016\/j.patrec.2020.03.030","journal-title":"Pattern Recogn Lett"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-023-10439-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-023-10439-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-023-10439-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T02:25:14Z","timestamp":1711160714000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-023-10439-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":76,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10439"],"URL":"https:\/\/doi.org\/10.1007\/s10664-023-10439-z","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,23]]},"assertion":[{"value":"17 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","order":2,"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 they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"50"}}