{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:33:57Z","timestamp":1783096437053,"version":"3.54.6"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"NSF-Hebei","award":["F2022208009"],"award-info":[{"award-number":["F2022208009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07248-8","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T12:02:44Z","timestamp":1650628964000},"page":"15441-15455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel approach of data race detection based on CNN-BiLSTM hybrid neural network"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8641-2660","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"7248_CR1","doi-asserted-by":"crossref","unstructured":"Gu Y, Mellor-Crummey J (2018) Dynamic data race detection for OpenMP programs. In: International conference for high performance computing, networking, storage and analysis, pp 767\u2013778. IEEE","DOI":"10.1109\/SC.2018.00064"},{"key":"7248_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3276514","volume":"2","author":"S Blackshear","year":"2018","unstructured":"Blackshear S, Gorogiannis N, O\u2019Hearn PW, Sergey I (2018) RacerD: compositional static race detection. Proc ACM Program Lang 2:1\u201328","journal-title":"Proc ACM Program Lang"},{"issue":"1","key":"7248_CR3","doi-asserted-by":"publisher","first-page":"186","DOI":"10.4149\/cai_2018_1_186","volume":"37","author":"A Sen","year":"2018","unstructured":"Sen A, Kalaci O (2018) Hybrid data race detection for multicore software. Comput Inform 37(1):186\u2013212","journal-title":"Comput Inform"},{"key":"7248_CR4","doi-asserted-by":"crossref","unstructured":"Said M, Wang C, Yang Z, Sakallah K (2011) Generating data race witnesses by an SMT-based analysis. In: NASA formal methods symposium. Springer, Berlin, Heidelberg, pp 313\u2013327","DOI":"10.1007\/978-3-642-20398-5_23"},{"key":"7248_CR5","doi-asserted-by":"crossref","unstructured":"Huang J, Meredith PON, Rosu G (2014) Maximal sound predictive race detection with control flow abstraction. In: Proceedings of the 35th ACM SIGPLAN conference on programming language design and implementation, pp 337\u2013348","DOI":"10.1145\/2594291.2594315"},{"key":"7248_CR6","doi-asserted-by":"crossref","unstructured":"Peng Y, DeLozier C, Eizenberg A, Mansky W, Devietti J (2018) Slimfast: reducing metadata redundancy in sound and complete dynamic data race detection. In: 2018 IEEE international parallel and distributed processing symposium (IPDPS), pp 835\u2013844. IEEE","DOI":"10.1109\/IPDPS.2018.00093"},{"key":"7248_CR7","doi-asserted-by":"crossref","unstructured":"Voung JW, Jhala R, Lerner S (2007) RELAY: static race detection on millions of lines of code. In: Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, pp 205\u2013214","DOI":"10.1145\/1287624.1287654"},{"key":"7248_CR8","doi-asserted-by":"crossref","unstructured":"Taft ST, Schanda F, Moy Y (2016) High-integrity multitasking in spark: static detection of data races and locking cycles. In: 2016 IEEE 17th international symposium on high assurance systems engineering (HASE), pp 238\u2013239. IEEE","DOI":"10.1109\/HASE.2016.54"},{"issue":"1","key":"7248_CR9","first-page":"61","volume":"39","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Liang Y, Zhang D, Sun S (2019) Data race detection approach in concurrent programs. J Comput Appl 39(1):61","journal-title":"J Comput Appl"},{"key":"7248_CR10","doi-asserted-by":"crossref","unstructured":"Yang, Z, Yu Z, Su X, Ma P (2016) RaceTracker: effective and efficient detection of data races. In: 2016 17th IEEE\/ACIS international conference on software engineering, artificial intelligence, networking and parallel\/distributed computing (SNPD), pp 293\u2013300. IEEE","DOI":"10.1109\/SNPD.2016.7515908"},{"issue":"3","key":"7248_CR11","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TR.2018.2832226","volume":"67","author":"J Yang","year":"2018","unstructured":"Yang J, Jiang B, Chan WK (2018) Histlock+: precise memory access maintenance without lockset comparison for complete hybrid data race detection. IEEE Trans Reliab 67(3):786\u2013801","journal-title":"IEEE Trans Reliab"},{"issue":"6","key":"7248_CR12","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/TSE.2018.2791521","volume":"45","author":"T Yu","year":"2018","unstructured":"Yu T, Wen W, Han X, Hayes JH (2018) Conpredictor: concurrency defect prediction in real-world applications. IEEE Trans Softw Eng 45(6):558\u2013575","journal-title":"IEEE Trans Softw Eng"},{"key":"7248_CR13","doi-asserted-by":"crossref","unstructured":"Li, J, He P, Zhu J, Lyu MR (2017) Software defect prediction via convolutional neural network. In: 2017 IEEE international conference on software quality, reliability and security (QRS), pp 318\u2013328. IEEE.","DOI":"10.1109\/QRS.2017.42"},{"key":"7248_CR14","unstructured":"Tehrani A, Khaleel M, Akbari R, Jannesari A (2019) DeepRace: finding data race bugs via deep learning.\u00a0arXiv preprint arXiv:1907.07110"},{"key":"7248_CR15","unstructured":"IBM. T.J Watson libraries for analysis (WALA). http:\/\/wala.sourceforge.net"},{"key":"7248_CR16","doi-asserted-by":"publisher","first-page":"20861","DOI":"10.1109\/ACCESS.2021.3055831","volume":"9","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Liu H, Qiao L (2021) Context-sensitive data race detection for concurrent programs. IEEE Access 9:20861\u201320867","journal-title":"IEEE Access"},{"key":"7248_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","volume":"465","author":"G Douzas","year":"2018","unstructured":"Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci 465:1\u201320","journal-title":"Inf Sci"},{"issue":"3","key":"7248_CR18","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/make1030048","volume":"1","author":"M Rhanoui","year":"2019","unstructured":"Rhanoui M, Mikram M, Yousfi S, Barzali S (2019) A CNN-BiLSTM model for document-level sentiment analysis. Mach Learn Knowl Extr 1(3):832\u2013847","journal-title":"Mach Learn Knowl Extr"},{"key":"7248_CR19","doi-asserted-by":"crossref","unstructured":"Blackburn SM, Garner R, Hoffmann C, Khang AM, McKinley KS, Bentzur R, Diwan A, Feinberg D, Frampton D, Guyer SZ, Hirzel M, Wiedermann B (2006) The DaCapo benchmarks: java benchmarking development and analysis. In: Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications, pp 169\u2013190","DOI":"10.1145\/1167515.1167488"},{"key":"7248_CR20","unstructured":"Farchi E, Nir Y, Ur S (2003) Concurrent bug patterns and how to test them. In: Proceedings international parallel and distributed processing symposium. IEEE"},{"key":"7248_CR21","doi-asserted-by":"crossref","unstructured":"Gao J, Yang X, Jiang Y, Liu H, Ying W, Zhang X (2018) Jbench: a dataset of data races for concurrency testing. In: Proceedings of the 15th international conference on mining software repositories, pp 6\u20139","DOI":"10.1145\/3196398.3196451"},{"key":"7248_CR22","doi-asserted-by":"crossref","unstructured":"Smith LA, Bull JM, Obdrizalek J (2001) A parallel Java Grande benchmark suite. In: SC\u201901: proceedings of the 2001 ACM\/IEEE conference on supercomputing, pp 6\u20136. IEEE","DOI":"10.1145\/582034.582042"},{"issue":"6","key":"7248_CR23","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1093\/comjnl\/bxu119","volume":"59","author":"M Yu","year":"2016","unstructured":"Yu M, Bae DH (2016) SimpleLock+: fast and accurate hybrid data race detection. Comput J 59(6):793\u2013809","journal-title":"Comput J"},{"issue":"10","key":"7248_CR24","first-page":"315","volume":"47","author":"MK Li","year":"2020","unstructured":"Li MK, Zheng QS, Wang L (2020) A dynamic hybrid data race detection algorithm based on sampling technique. Comput Sci 47(10):315\u2013321","journal-title":"Comput Sci"},{"issue":"7","key":"7248_CR25","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.13328\/j.cnki.jos.006260","volume":"32","author":"F Gao","year":"2021","unstructured":"Gao F, Wang Y, Zhou J et al (2021) High precision large-scale program data race detection method. J Softw 32(7):2039\u20132055. https:\/\/doi.org\/10.13328\/j.cnki.jos.006260","journal-title":"J Softw"},{"key":"7248_CR26","doi-asserted-by":"crossref","unstructured":"Kusano M., Wang C (2013) CCmutator: a mutation generator for concurrency constructs in multithreaded C\/C++ applications. In: 28th IEEE\/ACM international conference on automated software engineering (ASE), pp 722\u2013725. IEEE","DOI":"10.1109\/ASE.2013.6693142"},{"issue":"10","key":"7248_CR27","first-page":"804","volume":"60","author":"S Jiaze","year":"2020","unstructured":"Jiaze S, Jiawei Y, Zijiang Y (2020) Multithreaded program data race random forest instruction level detection model. J Tsinghua Univ (NATURAL SCIENCE EDITION) 60(10):804\u2013813","journal-title":"J Tsinghua Univ (NATURAL SCIENCE EDITION)"},{"key":"7248_CR28","doi-asserted-by":"crossref","unstructured":"Ali A, Zhu Y, Chen Q et al (2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS). IEEE","DOI":"10.1109\/ICPADS47876.2019.00025"},{"issue":"20","key":"7248_CR29","doi-asserted-by":"publisher","first-page":"31401","DOI":"10.1007\/s11042-020-10486-4","volume":"80","author":"A Ali","year":"2021","unstructured":"Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multim Tools Appl 80(20):31401\u201331433. https:\/\/doi.org\/10.1007\/s11042-020-10486-4","journal-title":"Multim Tools Appl"},{"key":"7248_CR30","unstructured":"The\u00a0Python\u00a0Deep\u00a0Learning\u00a0library (Keras). https:\/\/keras.io\/"},{"key":"7248_CR31","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, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"7248_CR32","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. Eprint Arxiv"},{"key":"7248_CR33","doi-asserted-by":"crossref","unstructured":"Cho K, Merrienboer BV, Gulcehre C et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. Comput Sci","DOI":"10.3115\/v1\/D14-1179"},{"issue":"5","key":"7248_CR34","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1016\/j.ipm.2019.02.007","volume":"56","author":"V Makarenkov","year":"2019","unstructured":"Makarenkov V, Guy I, Hazon N, Meisels T, Shapira B, Rokach L (2019) Implicit dimension identification in user-generated text with LSTM networks. Inf Process Manag 56(5):1880\u20131893","journal-title":"Inf Process Manag"},{"key":"7248_CR35","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338","journal-title":"Neurocomputing"},{"key":"7248_CR36","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"7248_CR37","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fdez I, Canosa A, Mucientes M et al (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1\u20138. IEEE","DOI":"10.1109\/FUZZ-IEEE.2015.7337889"},{"key":"7248_CR38","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-1-4614-1412-4_35","volume-title":"Selected works of E. L. Lehmann","author":"JL Hodges","year":"2012","unstructured":"Hodges JL, Lehmann EL (2012) Rank methods for combination of independent experiments in analysis of variance. In: Rojo J (ed) Selected works of E. L. Lehmann. Springer, Boston, MA, pp 403\u2013418"},{"issue":"2","key":"7248_CR39","first-page":"65","volume":"6","author":"S Holm","year":"1979","unstructured":"Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65\u201370","journal-title":"Scand J Stat"},{"key":"7248_CR40","first-page":"281","volume":"16","author":"G Hamerly","year":"2004","unstructured":"Hamerly G, Elkan C (2004) Learning the k in k-means. Adv Neural Inf Process Syst 16:281\u2013288","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07248-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07248-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07248-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:20:07Z","timestamp":1662056407000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07248-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,22]]},"references-count":40,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7248"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07248-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,22]]},"assertion":[{"value":"22 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}