[1] [1] Kamei Y, Shihab E, Adams B, et al. A large-scale empirical study of just-in-time quality assurance[J]. IEEE Transactions on Software Engineering, 2013, 39(6): 757-772.

[3] [3] Yang X L, Lo D, Xia X, et al. Deep learning for just-in-time defect prediction[C]//IEEE International Conference on Software Quality, 2015: 17-26.

[4] [4] Hoang T, Dam H K, Kamei Y, et al. DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction[C]//16th International Conference on Mining Software Repositories, 2019: 34-45.

[6] [6] Lessmann S, Baesens B, Mues C, et al. Benchmarking classification models for software defect prediction: A proposed framework and novel findings[J]. IEEE Transactions on Software Engineering, 2008, 34(4): 485-496.

[7] [7] Xia X, Lo D, Pan S J, et al. HYDRA: Massively compositional model for cross-project defect prediction[J]. IEEE Transactions on Software Engineering, 2016, 42(10): 977-998.

[8] [8] Koru A G, Zhang D S, Emam K E, et al. An investigation into the functional form of the size-defect relationship for software modules[J]. IEEE Transactions on Software Engineering, 2009, 35(2): 293-304.

[9] [9] Kim S H, Whitehead E J, Yi Z. Classifying software changes: Clean or buggy?[J]. IEEE Transactions on Software Engineering, 2008, 34(2): 181-196.

[10] [10] Shihab E, Hassan A E, Adams B, et al. An industrial study on the risk of software changes[C]//20th International Symposium on Foundations of Software Engineering, 2012: 1-11.

[11] [11] Shivaji S, Whitehead E J, Akella R, et al. Reducing features to improve code change-based bug prediction[J]. IEEE Transactions on Software Engineering, 2013, 39(4): 552-569.

[12] [12] Qiao L, Wang Y. Effort-aware and just-in-time defect prediction with neural network[J]. PLoS One, 2019, 14(2): e0211359.

[13] [13] Zhu K, Zhang N, Ying S, et al. Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network[J]. IET Software, 2020, 14(3): 185-195.

[14] [14] Mori T, Uchihira N. Balancing the trade-off between accuracy and interpretability in software defect prediction[J]. Empirical Software Engineering, 2019, 24(2): 779-825.

[15] [15] Jiarpakdee J, Tantithamthavorn C, Dam H K, et al. An empirical study of model-agnostic techniques for defect prediction models[J]. IEEE Transactions on Software Engineering, 2020, 48(1): 166-185.

[18] [18] Esteves G, Figueiredo E, Veloso A, et al. Understanding machine learning software defect predictions[J]. Automated Software Engineering, 2020, 27(3/4): 369-392.

[19] [19] He Q, Shen B J, Chen Y T. Software defect prediction using semi-supervised learning with change burst information[C]//Computer Software & Applications Conference, 2016: 113-122.

[20] [20] Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117.

[21] [21] Yang Y B, Zhou Y M, Liu J P, et al. Effort-aware just-in-time defect prediction: Simple unsupervised models could be better than supervised models[C]//24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016: 157-168.

[22] [22] Fu W, Menzies T. Revisiting unsupervised learning for defect prediction[C]//11th Joint Meeting on Foundations of Software Engineering, 2017: 72-83.

[23] [23] Huang Q, Xia X, Lo D. Revisiting supervised and unsupervised models for effort-awarejust-in-time defect prediction[J]. Empirical Software Engineering, 2019, 24(5): 2823-2862.

[24] [24] Liu J P, Zhou Y M, Yang Y B, et al. Code churn: A neglected metric in effort-aware Just-in-time defect prediction[C]//ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2017: 11-19.

[25] [25] Guo Y C, Shepperd M, Li N. Bridging effort-aware prediction and strong classification[C]//Software Engineering, 2018: 325-326.