ReSym: Harnessing LLMs to Recover Variable and Data Structure Symbols from Stripped Binaries. Danning Xie, Zhuo Zhang, Nan Jiang, Xiangzhe Xu, Lin Tan, and Xiangyu Zhang. Won Distinguished Paper Award!

When Automated Program Repair Meets Regression Testing---An Extensive Study on 2 Million Patches. Yiling Lou, Jun Yang, Samuel Benton, Dan Hao, Lin Tan, Zhenpeng Chen, Lu Zhang, and Lingming Zhang.

CEDAR: Continuous Testing of Deep Learning Libraries. Danning Xie, Jiannan Wang, Hung Viet Pham, Lin Tan, Yu Guo, Adnan Aziz, and Erik Meijer.

Vicious Cycles in Distributed Software Systems. Shangshu Qian, Wen Fan, Lin Tan, and Yongle Zhang. Acceptance Rate: 21%

How Effective are Neural Networks for Fixing Security Vulnerabilities? Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena Shah. Acceptance Rate: 23% (49/215)

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!