{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:06:19Z","timestamp":1764997579349},"reference-count":14,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2024,2,1]]},"DOI":"10.1587\/transinf.2023edl8052","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T22:14:51Z","timestamp":1706739291000},"page":"234-238","source":"Crossref","is-referenced-by-count":4,"title":["A Data Augmentation Method for Fault Localization with Fault Propagation Context and VAE"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Zhuo","family":"ZHANG","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University"}]},{"given":"Donghui","family":"LI","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University"}]},{"given":"Lei","family":"XIA","sequence":"additional","affiliation":[{"name":"No.83 Army Joint and Truma Disease Treatment Centre of PLA"}]},{"given":"Ya","family":"LI","sequence":"additional","affiliation":[{"name":"Ningbo Artificial Intelligence Institute, Shanghai Jiaotong University"}]},{"given":"Xiankai","family":"MENG","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Polytechnic University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] X. Xie, T.Y. Chen, F.-C. Kuo, and B. Xu, \u201cA theoretical analysis of the risk evaluation formulas for spectrum-based fault localization,\u201d ACM Transactions on Software Engineering and Methodology (TOSEM), vol.22, no.4, p.31, 2013. 10.1145\/2522920.2522924","DOI":"10.1145\/2522920.2522924"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] Z. Zhang, Y. Lei, X. Mao, M. Yan, L. Xu, and X. Zhang, \u201cA study of effectiveness of deep learning in locating real faults,\u201d Information and Software Technology, vol.131, p.106486, 2021. 10.1016\/j.infsof.2020.106486","DOI":"10.1016\/j.infsof.2020.106486"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] W.E. Wong, R. Gao, Y. Li, A. Rui, and F. Wotawa, \u201cA survey on software fault localization,\u201d IEEE Trans. Softw. Eng. (TSE), vol.42, no.8, pp.707-740, 2016. 10.1109\/tse.2016.2521368","DOI":"10.1109\/TSE.2016.2521368"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] M. B\u00f6hme, C. Geethal, and V.-T. Pham, \u201cHuman-in-the-loop automatic program repair,\u201d 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST), pp.274-285, 2020. 10.1109\/icst46399.2020.00036","DOI":"10.1109\/ICST46399.2020.00036"},{"key":"5","unstructured":"[5] G. An and S. Yoo, \u201cHuman-in-the-loop fault localisation using efficient test prioritisation of generated tests,\u201d CoRR, vol.abs\/2104.06641, 2021."},{"key":"6","unstructured":"[6] C. Doersch, \u201cTutorial on variational autoencoders,\u201d arXiv preprint arXiv:1606.05908, 2016."},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] B. Xu, J. Qian, X. Zhang, Z. Wu, and L. Chen, \u201cA brief survey of program slicing,\u201d ACM SIGSOFT Software Engineering Notes, vol.30, no.2, pp.1-36, 2005. 10.1145\/1050849.1050865","DOI":"10.1145\/1050849.1050865"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] L. Zhang, L. Yan, Z. Zhang, J. Zhang, W.K. Chan, and Z. Zheng, \u201cA theoretical analysis on cloning the failed test cases to improve spectrum-based fault localization,\u201d Journal of Systems and Software, vol.129, pp.35-57, 2017. 10.1016\/j.jss.2017.04.017","DOI":"10.1016\/j.jss.2017.04.017"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] S. Pearson, J. Campos, R. Just, G. Fraser, R. Abreu, M.D. Ernst, D. Pang, and B. Keller, \u201cEvaluating and Improving Fault Localization,\u201d 2017 IEEE\/ACM 39th International Conference on Software Engineering (ICSE), 2017. 10.1109\/icse.2017.62","DOI":"10.1109\/ICSE.2017.62"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] X. Li, W. Li, Y. Zhang, and L. Zhang, \u201cDeepFL: Integrating multiple fault diagnosis dimensions for deep fault localization,\u201d Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019), pp.169-180, 2019. 10.1145\/3293882.3330574","DOI":"10.1145\/3293882.3330574"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] C. Parnin and A. Orso, \u201cAre automated debugging techniques actually helping programmers?,\u201d International Symposium on Software Testing and Analysis (ISSTA 2011), pp.199-209, 2011. 10.1145\/2001420.2001445","DOI":"10.1145\/2001420.2001445"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] H. Xie, Y. Lei, M. Yan, Y. Yu, X. Xia, and X. Mao, \u201cA universal data augmentation approach for fault localization,\u201d Proceedings of the 44th International Conference on Software Engineering, pp.48-60, 2022. 10.1145\/3510003.3510136","DOI":"10.1145\/3510003.3510136"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] Y. Lei, C. Liu, H. Xie, S. Huang, M. Yan, and Z. Xu, \u201cBCL-FL: A data augmentation approach with between-class learning for fault localization,\u201d 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp.289-300, IEEE, 2022. 10.1109\/saner53432.2022.00045","DOI":"10.1109\/SANER53432.2022.00045"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Y. Lei, T. Wen, H. Xie, L. Fu, C. Liu, L. Xu, and H. Sun, \u201cMitigating the effect of class imbalance in fault localization using context-aware generative adversarial network,\u201d 2023 IEEE\/ACM 31st International Conference on Program Comprehension (ICPC), 2023. 10.1109\/icpc58990.2023.00045","DOI":"10.1109\/ICPC58990.2023.00045"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/2\/E107.D_2023EDL8052\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T04:17:10Z","timestamp":1706933830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/2\/E107.D_2023EDL8052\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,1]]},"references-count":14,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edl8052","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,1]]},"article-number":"2023EDL8052"}}