Impact of Code Language Models on Automated Program Repair. Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan. Acceptance Rate: 26% (208/796)

KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair. Nan Jiang, Thibaud Lutellier, Yiling Lou, Lin Tan, Dan Goldwasser, and Xiangyu Zhang. Acceptance Rate: 26% (208/796)

Revisiting Learning-based Commit Message Generation Jinhao Dong, Yiling Lou, Dan Hao, and Lin Tan. Acceptance Rate: 26% (208/796)

DisGUIDE: Disagreement-Guided Data-Free Model Extraction. (Oral Presentation) Jonathan Rosenthal, Eric Enouen, Hung Viet Pham, and Lin Tan. Acceptance Rate: 19.6%

DocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions. Danning Xie, Yitong Li, Mijung Kim, Hung Viet Pham, Lin Tan, Xiangyu Zhang, Mike Godfrey. Acceptance Rate: 24% (61/250)

EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries. Jiannan Wang, Thibaud Lutellier, Shangshu Qian, Hung Viet Pham, and Lin Tan. Acceptance Rate: 26% (197/751)

Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, and Sameena Shah. Acceptance Rate: 26%

Exposing Numerical Bugs in Deep Learning via Gradient Back-propagation. Ming Yan, Junjie Chen, Xiangyu Zhang, Lin Tan, Gan Wang, and Zan Wang. Acceptance Rate: 24.5% (97/396)

DEVIATE: A Deep Learning Variance Testing Framework. Hung Viet Pham, Mijung Kim, Lin Tan, Yaoliang Yu, and Nachiappan Nagappan.

CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. Nan Jiang, Thibaud Lutellier, and Lin Tan. Acceptance Rate: 22% (138/615)

Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, and Nachiappan Nagappan. Acceptance Rate: 22.5% (93/414) Won ACM SIGSOFT Distinguished Paper Award!