activity
20202026
most citedOn the Evaluation of Neural Code Summarization

87 citations · 168 across the 51 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.SE2023

Boosting Redundancy-based Automated Program Repair by Fine-grained Pattern Mining

Jiajun Jiang, Fengjie Li, Zijie Zhao +5

Redundancy-based automated program repair (APR), which generates patches by referencing existing source code, has gained much attention since they are effective in repairing real-w…

cs.SE2023

OrdinalFix: Fixing Compilation Errors via Shortest-Path CFL Reachability

Wenjie Zhang, Guancheng Wang, Junjie Chen +3

The development of correct and efficient software can be hindered by compilation errors, which must be fixed to ensure the code's syntactic correctness and program language constra…

cs.SE2023★ 1 cited

COCO: Testing Code Generation Systems via Concretized Instructions

Ming Yan, Junjie Chen, Jie M. Zhang +3

Code generation systems have been extensively developed in recent years to generate source code based on natural language instructions. However, despite their advancements, these s…

cs.LG2023

Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing

Dong Huang, Qingwen Bu, Yichao Fu +3

Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect…

cs.SE2023

Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems

Zeyan Li, Junjie Chen, Yihao Chen +9

Localizing root causes for multi-dimensional data is critical to ensure online service systems' reliability. When a fault occurs, only the measure values within specific attribute…

cs.PL2023★ 2 cited

Compiler Auto-tuning through Multiple Phase Learning

Mingxuan Zhu, Dan Hao, Junjie Chen

Widely used compilers like GCC and LLVM usually have hundreds of optimizations controlled by optimization flags, which are enabled or disabled during compilation to improve runtime…