Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes. - Jingzhi Gong; Tao Chen 001 (MSR, 2022)

Predicting Software Performance with Divide-and-Learn. - Jingzhi Gong; Tao Chen 001 (ESEC/SIGSOFT FSE, 2023)

Pushing the Boundary: Specialising Deep Configuration Performance Learning. - Jingzhi Gong (CoRR, 2024)

GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation. - Jingzhi Gong; Sisi Li; Giordano d'Aloisio; Zishuo Ding; Yulong Ye; William B. Langdon; Federica Sarro (CoRR, 2024)

Predicting Configuration Performance in Multiple Environments with Sequential Meta-Learning. - Jingzhi Gong; Tao Chen 001 (Proc. ACM Softw. Eng., 2024)

Language Models for Code Optimization: Survey, Challenges and Future Directions. - Jingzhi Gong; Vardan Voskanyan; Paul Brookes; Fan Wu 009; Wei Jie; Jie Xu 007; Rafail Giavrimis; Mike Basios; Leslie Kanthan; Zheng Wang 001 (CoRR, 2025)

Deep Configuration Performance Learning: A Systematic Survey and Taxonomy. - Jingzhi Gong; Tao Chen 001 (ACM Trans. Softw. Eng. Methodol., 2025)

Dividable Configuration Performance Learning. - Jingzhi Gong; Tao Chen 001; Rami Bahsoon (IEEE Trans. Software Eng., 2025)

Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design. - Jingzhi Gong (CoRR, 2025)

Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning. - Pengzhou Chen; Jingzhi Gong; Tao Chen 001 (IEEE Trans. Software Eng., 2025)

Ensemble Learning for Large Language Models in Text and Code Generation: A Survey. - Mari Ashiga; Wei Jie; Fan Wu 009; Vardan Voskanyan; Fateme Dinmohammadi; Paul Brookes; Jingzhi Gong; Zheng Wang 001 (CoRR, 2025)

Learning Software Bug Reports: A Systematic Literature Review. - Guoming Long; Jingzhi Gong; Hui Fang; Tao Chen (CoRR, 2025)

Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning. - Zezhen Xiang; Jingzhi Gong; Tao Chen (CoRR, 2025)