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[62] Fan YQ, Cao XY, Xu J, Xu SH, Yang HJ. High-frequency keywords to predict defects for Android applications. In: Proc. of the 42nd
IEEE Annual Computer Software and Applications Conf. (COMPSAC). Tokyo: IEEE, 2018. 442–447. [doi: 10.1109/COMPSAC.2018.
10273]
[63] Cheng T, Zhao KS, Sun S, Mateen M, Wen JH. Effort-aware cross-project just-in-time defect prediction framework for mobile APPs.
Frontiers of Computer Science, 2022, 16(6): 166207. [doi: 10.1007/s11704-021-1013-5]
[64] Hu XY, Chen X, Xia HL, Gu YF. Interpretable method of just-in-time defect prediction model for mobile APP. Application Research of
Computers, 2022, 39(7): 2104–2108 (in Chinese with English abstract). [doi: 10.19734/j.issn.1001-3695.2021.12.0679]
[65] McIntosh S, Kamei Y. Are fix-inducing changes a moving target? A longitudinal case study of just-in-time defect prediction. In: Proc. of
the 40th IEEE/ACM Int’l Conf. on Software Engineering. Gothenburg: IEEE, 2018. 560. [doi: 10.1145/3180155.3182514]
[66] Hassan AE. Predicting faults using the complexity of code changes. In: Proc. of the 31st IEEE Int’l Conf. on Software Engineering.
Vancouver: IEEE, 2009. 78–88. [doi: 10.1109/ICSE.2009.5070510]
[67] Moser R, Pedrycz W, Succi G. A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction.
In: Proc. of the 30th Int’l Conf. on Software Engineering (ICSE). Leipzig: ACM, 2008. 181–190. [doi: 10.1145/1368088.1368114]
[68] Guo PJ, Zimmermann T, Nagappan N, Murphy B. Characterizing and predicting which bugs get fixed: An empirical study of Microsoft
Windows. In: Proc. of the 32nd ACM/IEEE Int’l Conf. on Software Engineering (ICSE). Cape Town: ACM, 2010. 495–504. [doi: 10.
1145/1806799.1806871]
[69] Purushothaman R, Perry DE. Toward understanding the rhetoric of small source code changes. IEEE Trans. on Software Engineering,
2005, 31(6): 511–526. [doi: 10.1109/TSE.2005.74]
[70] Matsumoto S, Kamei Y, Monden A, Matsumoto KI, Nakamura M. An analysis of developer metrics for fault prediction. In: Proc. of the
6th Int’l Conf. on Predictive Models in Software Engineering. Timişoara: ACM, 2010. 18. [doi: 10.1145/1868328.1868356]
[71] Spadini D, Aniche M, Bacchelli A. PyDriller: Python framework for mining software repositories. In: Proc. of the 26th ACM Joint
Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Lake Buena Vista: ACM,
2018. 908–911. [doi: 10.1145/3236024.3264598]
[72] Tantithamthavorn C, Hassan AE. An experience report on defect modelling in practice: Pitfalls and challenges. In: Proc. of the 40th Int’l
Conf. on Software Engineering: Software Engineering in Practice. Gothenburg: ACM, 2018. 286–295. [doi: 10.1145/3183519.3183547]
[73] Jiarpakdee J, Tantithamthavorn C, Treude C. AutoSpearman: Automatically mitigating correlated software metrics for interpreting defect
models. In Proc. of the 2018 IEEE Int’l Conf. on Software Maintenance and Evolution (ICSME). Madrid: IEEE, 2018. 92–103. [doi: 10.
1109/ICSME.2018.00018]
[74] Tan M, Tan L, Dara S, Mayeux C. Online defect prediction for imbalanced data. In: Proc. of the 37th IEEE/ACM IEEE Int’l Conf. on
Software Engineering. Florence: IEEE, 2015. 99–108. [doi: 10.1109/ICSE.2015.139]
[75] Liaw A, Wiener M. Classification and regression by randomForest. R News, 2002, 2(3): 18–22.
[76] Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and
System Sciences, 1997, 55(1): 119–139. [doi: 10.1006/jcss.1997.1504]
[77] Liu XT, Guo ZQ, Liu SR, Zhang P, Lu HM, Zhou YM. Comparing software defect prediction models: Research problem, progress, and
challenges. Ruan Jian Xue Bao/Journal of Software, 2023, 34(2): 582–624 (in Chinese with English abstract). http://www.jos.org.cn/1000-
9825/6714.htm [doi: 10.13328/j.cnki.jos.006714]
[78] Li ZQ, Jing XY, Zhu XK, Zhang HY, Xu BW, Ying S. On the multiple sources and privacy preservation issues for heterogeneous defect
prediction. IEEE Trans. on Software Engineering, 2019, 45(4): 391–411. [doi: 10.1109/TSE.2017.2780222]
[79] Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K. An empirical comparison of model validation techniques for defect
prediction models. IEEE Trans. on Software Engineering, 2017, 43(1): 1–18. [doi: 10.1109/TSE.2016.2584050]
[80] Li ZQ, Jing XY, Zhu XK, Zhang HY, Xu BW, Ying S. Heterogeneous defect prediction with two-stage ensemble learning. Automated
Software Engineering, 2019, 26(3): 599–651. [doi: 10.1007/s10515-019-00259-1]
[81] Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Proc. of the 31st Int’l Conf. on Neural Information
Processing Systems. Long Beach: Curran Associates Inc., 2017. 4768–4777.
[82] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial
Intelligence Research, 2002, 16: 321–357. [doi: 10.1613/jair.953]
[83] Lunardon N, Menardi G, Torelli N. ROSE: A package for binary imbalanced learning. The R Journal, 2014, 6(1): 79–89. [doi: 10.32614/
RJ-2014-008]
[84] Rosa G, Pascarella L, Scalabrino S, Tufano R, Bavota G, Lanza M, Oliveto R. Evaluating SZZ implementations through a developer-
informed oracle. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on Software Engineering (ICSE). Madrid: IEEE, 2021. 436–447. [doi: 10.
1109/ICSE43902.2021.00049]

