{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T22:00:39Z","timestamp":1792706439147,"version":"4.5.0"},"reference-count":95,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            Recent years have experienced sustained focus in research on software defect prediction that aims to predict the likelihood of software defects. Moreover, with the increased interest in continuous deployment, a variant of software defect prediction called\n            <jats:italic>Just-in-Time Software Defect Prediction<\/jats:italic>\n            (JIT-SDP) focuses on predicting whether each incremental software change is defective. JIT-SDP is unique in that it consists of two interconnected data streams, one consisting of the arrivals of software changes stemming from design and implementation, and the other the (defective or clean) labels of software changes resulting from quality assurance processes.\n          <\/jats:p>\n          <jats:p>We present a systematic survey of 67 JIT-SDP studies with the objective to help researchers advance the state of the art in JIT-SDP and to help practitioners become familiar with recent progress. We summarize best practices in each phase of the JIT-SDP workflow, carry out a meta-analysis of prior studies, and suggest future research directions. Our meta-analysis of JIT-SDP studies indicates, among other findings, that the predictive performance correlates with change defect ratio, suggesting that JIT-SDP is most performant in projects that experience relatively high defect ratios. Future research directions for JIT-SDP include situating each technique into its application domain, reliability-aware JIT-SDP, and user-centered JIT-SDP.<\/jats:p>","DOI":"10.1145\/3567550","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T13:05:48Z","timestamp":1666011948000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":131,"title":["A Systematic Survey of Just-in-Time Software Defect Prediction"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8871-4064","authenticated-orcid":false,"given":"Yunhua","family":"Zhao","sequence":"first","affiliation":[{"name":"CUNY Graduate Center, New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7799-2026","authenticated-orcid":false,"given":"Kostadin","family":"Damevski","sequence":"additional","affiliation":[{"name":"Virginia Commonwealth University, Richmond, VA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9840-4876","authenticated-orcid":false,"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"CUNY Brooklyn College, Brooklyn, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1007\/s00521-021-06659-3","article-title":"Just-in-time software defect prediction using deep temporal convolutional networks","author":"Ardimento Pasquale","year":"2022","unstructured":"Pasquale Ardimento, Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, and Martina Iammarino. 2022. 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IEEE, Los Alamitos, CA, 11\u201319."},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.11.023"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2693980"},{"issue":"6","key":"e_1_3_3_49_2","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1109\/TSE.2012.83","article-title":"Local versus global lessons for defect prediction and effort estimation","volume":"39","author":"Menzies Tim","year":"2012","unstructured":"Tim Menzies, Andrew Butcher, David Cok, Andrian Marcus, Lucas Layman, Forrest Shull, Burak Turhan, and Thomas Zimmermann. 2012. Local versus global lessons for defect prediction and effort estimation. 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IEEE, Los Alamitos, CA, 609\u2013612."},{"key":"e_1_3_3_53_2","first-page":"380","volume-title":"Proceedings of the 2018 IEEE 25th International Conference on Software Analysis, Evolution, and Reengineering (SANER\u201918)","author":"Neto Edmilson Campos","year":"2018","unstructured":"Edmilson Campos Neto, Daniel Alencar da Costa, and Uir\u00e1 Kulesza. 2018. The impact of refactoring changes on the SZZ algorithm: An empirical study. In Proceedings of the 2018 IEEE 25th International Conference on Software Analysis, Evolution, and Reengineering (SANER\u201918). IEEE, Los Alamitos, CA, 380\u2013390."},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09772-z"},{"key":"e_1_3_3_55_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1145\/3196398.3196466","volume-title":"Proceedings of the 15th International Conference on Mining Software Repositories","author":"Paixao Matheus","year":"2018","unstructured":"Matheus Paixao, Jens Krinke, Donggyun Han, and Mark Harman. 2018. CROP: Linking code reviews to source code changes. In Proceedings of the 15th International Conference on Mining Software Repositories. 46\u201349."},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2018.12.001"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_3_58_2","article-title":"JITLine: A simpler, better, faster, finer-grained just-in-time defect prediction","author":"Pornprasit Chanathip","year":"2021","unstructured":"Chanathip Pornprasit and Chakkrit Tantithamthavorn. 2021. JITLine: A simpler, better, faster, finer-grained just-in-time defect prediction. arXiv preprint arXiv:2103.07068 (2021).","journal-title":"arXiv preprint arXiv:2103.07068"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211359"},{"issue":"5","key":"e_1_3_3_60_2","first-page":"1","article-title":"Evaluating the impact of falsely detected performance bug-inducing changes in JIT models","volume":"26","author":"Quach Sophia","year":"2021","unstructured":"Sophia Quach, Maxime Lamothe, Bram Adams, Yasutaka Kamei, and Weiyi Shang. 2021. Evaluating the impact of falsely detected performance bug-inducing changes in JIT models. Empirical Software Engineering 26, 5 (2021), 1\u201332.","journal-title":"Empirical Software Engineering"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2013.02.009"},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/2568225.2568269"},{"issue":"5","key":"e_1_3_3_63_2","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1162\/NECO_a_00947","article-title":"Parameter identifiability in statistical machine learning: A review","volume":"29","author":"Ran Zhi-Yong","year":"2017","unstructured":"Zhi-Yong Ran and Bao-Gang Hu. 2017. Parameter identifiability in statistical machine learning: A review. Neural Computation 29, 5 (2017), 1151\u20131203.","journal-title":"Neural Computation"},{"issue":"4","key":"e_1_3_3_64_2","article-title":"Watch out for extrinsic bugs! 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IEEE, Los Alamitos, CA."},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/1670679.1670680"},{"key":"e_1_3_3_67_2","unstructured":"SEI Authors. 2016. SEI CERT C Coding Standard: Rules for Developing Safe Reliable and Secure Systems . Software Engineering Institute Carnegie Mellon University."},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/1082983.1083147"},{"key":"e_1_3_3_69_2","first-page":"554","volume-title":"Proceedings of the 2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE\u201920)","author":"Tabassum Sadia","year":"2020","unstructured":"Sadia Tabassum, Leandro L. Minku, Danyi Feng, George G. Cabral, and Liyan Song. 2020. An investigation of cross-project learning in online just-in-time software defect prediction. In Proceedings of the 2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE\u201920). IEEE, Los Alamitos, CA, 554\u2013565."},{"key":"e_1_3_3_70_2","first-page":"99","volume-title":"Proceedings of the 2015 IEEE\/ACM 37th IEEE International Conference on Software Engineering","volume":"2","author":"Tan Ming","year":"2015","unstructured":"Ming Tan, Lin Tan, Sashank Dara, and Caleb Mayeux. 2015. Online defect prediction for imbalanced data. In Proceedings of the 2015 IEEE\/ACM 37th IEEE International Conference on Software Engineering, Vol. 2. IEEE, Los Alamitos, CA, 99\u2013108."},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3183519.3183547"},{"key":"e_1_3_3_72_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/ISSRE.2013.6698912","volume-title":"Proceedings of the 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE\u201913)","author":"Tarvo Alexander","year":"2013","unstructured":"Alexander Tarvo, Nachiappan Nagappan, Thomas Zimmermann, Thirumalesh Bhat, and Jacek Czerwonka. 2013. Predicting risk of pre-release code changes with Checkinmentor. In Proceedings of the 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE\u201913). IEEE, Los Alamitos, CA, 128\u2013137."},{"key":"e_1_3_3_73_2","first-page":"511","volume-title":"Proceedings of the 2021 IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER\u201921)","author":"Tessema Hailemelekot Demtse","year":"2021","unstructured":"Hailemelekot Demtse Tessema and Surafel Lemma Abebe. 2021. Enhancing just-in-time defect prediction using change request-based metrics. In Proceedings of the 2021 IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER\u201921). IEEE, Los Alamitos, CA, 511\u2013515."},{"key":"e_1_3_3_74_2","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/QRS51102.2020.00038","volume-title":"Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability, and Security (QRS\u201920)","author":"Tian Yuli","year":"2020","unstructured":"Yuli Tian, Ning Li, Jeff Tian, and Wei Zheng. 2020. How well just-in-time defect prediction techniques enhance software reliability? In Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability, and Security (QRS\u201920). IEEE, Los Alamitos, CA, 212\u2013221."},{"key":"e_1_3_3_75_2","first-page":"189","volume-title":"Proceedings of the 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER\u201916)","volume":"1","author":"Tourani Parastou","year":"2016","unstructured":"Parastou Tourani and Bram Adams. 2016. The impact of human discussions on just-in-time quality assurance: An empirical study on OpenStack and Eclipse. In Proceedings of the 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER\u201916), Vol. 1. IEEE, Los Alamitos, CA, 189\u2013200."},{"key":"e_1_3_3_76_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/ICSME46990.2020.00022","volume-title":"Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME\u201920)","author":"Trautsch Alexander","year":"2020","unstructured":"Alexander Trautsch, Steffen Herbold, and Jens Grabowski. 2020. Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction. In Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME\u201920). IEEE, Los Alamitos, CA, 127\u2013138."},{"issue":"11","key":"e_1_3_3_77_2","first-page":"1241","article-title":"Perceptions, expectations, and challenges in defect prediction","volume":"46","author":"Wan Zhiyuan","year":"2018","unstructured":"Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, and Xiaohu Yang. 2018. Perceptions, expectations, and challenges in defect prediction. IEEE Transactions on Software Engineering 46, 11 (2018), 1241\u20131266.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_3_3_78_2","article-title":"Explainable deep learning: A field guide for the uninitiated","author":"Xie Ning","year":"2020","unstructured":"Ning Xie, Gabrielle Ras, Marcel van Gerven, and Derek Doran. 2020. Explainable deep learning: A field guide for the uninitiated. arXiv preprint arXiv:2004.14545 (2020).","journal-title":"arXiv preprint arXiv:2004.14545"},{"issue":"1","key":"e_1_3_3_79_2","article-title":"Effort-aware just-in-time bug prediction for mobile apps via cross-triplet deep feature embedding","volume":"71","author":"Xu Zhou","year":"2022","unstructured":"Zhou Xu, Kunsong Zhao, Tao Zhang, Chunlei Fu, Meng Yan, Zhiwen Xie, Xiaohong Zhang, and Gemma Catolino. 2022. Effort-aware just-in-time bug prediction for mobile apps via cross-triplet deep feature embedding. IEEE Transactions on Reliability 71, 1 (2022), 204\u2013220.","journal-title":"IEEE Transactions on Reliability"},{"issue":"1","key":"e_1_3_3_80_2","article-title":"Just-in-time defect identification and localization: A two-phase framework","volume":"48","author":"Yan Meng","year":"2022","unstructured":"Meng Yan, Xin Xia, Yuanrui Fan, Ahmed E. Hassan, David Lo, and Shanping Li. 2022. Just-in-time defect identification and localization: A two-phase framework. IEEE Transactions on Software Engineering 48, 1 (2022), 82\u2013101.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_3_3_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417048"},{"key":"e_1_3_3_82_2","first-page":"1","volume-title":"Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM\u201917)","author":"Yang Limin","year":"2017","unstructured":"Limin Yang, Xiangxue Li, and Yu Yu. 2017. VulDigger: A just-in-time and cost-aware tool for digging vulnerability-contributing changes. In Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM\u201917). IEEE, Los Alamitos, CA, 1\u20137."},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.03.007"},{"key":"e_1_3_3_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2015.14"},{"key":"e_1_3_3_85_2","article-title":"Local versus global models for just-in-time software defect prediction","volume":"2019","author":"Yang Xingguang","year":"2019","unstructured":"Xingguang Yang, Huiqun Yu, Guisheng Fan, Kai Shi, and Liqiong Chen. 2019. Local versus global models for just-in-time software defect prediction. 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In Proceedings of the 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering. 42\u201347."},{"key":"e_1_3_3_89_2","first-page":"1","volume-title":"Proceedings of the 7th International Conference on Predictive Models in Software Engineering","author":"Zeller Andreas","year":"2011","unstructured":"Andreas Zeller, Thomas Zimmermann, and Christian Bird. 2011. Failure is a four-letter word: A parody in empirical research. In Proceedings of the 7th International Conference on Predictive Models in Software Engineering. 1\u20137."},{"key":"e_1_3_3_90_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464819"},{"key":"e_1_3_3_91_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-015-9396-2"},{"key":"e_1_3_3_92_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/978-3-030-16145-3_23","volume-title":"Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Zhang Wenzhou","year":"2019","unstructured":"Wenzhou Zhang, Weiwei Li, and Xiuyi Jia. 2019. Effort-aware tri-training for semi-supervised just-in-time defect prediction. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 293\u2013304."},{"issue":"5","key":"e_1_3_3_93_2","article-title":"A compositional model for effort-aware just-in-time defect prediction on Android apps","volume":"16","author":"Zhao Kunsong","year":"2021","unstructured":"Kunsong Zhao, Zhou Xu, Meng Yan, Lei Xue, Wei Li, and Gemma Catolino. 2021. A compositional model for effort-aware just-in-time defect prediction on Android apps. IET Software 16, 5 (2021), 1\u201320.","journal-title":"IET Software"},{"key":"e_1_3_3_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3060937"},{"key":"e_1_3_3_95_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-sen.2019.0278"},{"issue":"11","key":"e_1_3_3_96_2","first-page":"1968","article-title":"An empirical study of software change classification with imbalance data-handling methods","volume":"48","author":"Zhu Xiaoyan","year":"2018","unstructured":"Xiaoyan Zhu, Binbin Niu, E. 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