10 citations · 20 across the 7 of their papers we have counts for
8 papers
Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation
Jiajun Jiang, Sharon Zheng, Natan Vidra +1
AI coding agent benchmarks rank agents with the Chen et al. (2021) pass@k estimator, but current implementations misapply it: they set n to the number of unit tests in a single sub…
Towards Better Linux Kernel Fault Localization: Leveraging Contrastive Reasoning and Hierarchical Context Analysis
Haichi Wang, Ruiguo Yu, Yesong Pang +4
Debugging the Linux kernel remains a formidable challenge due to its vast codebase, complex architecture, and low-level programming intricacies. Effective fault localization (FL) i…
DESIL: Detecting Silent Bugs in MLIR Compiler Infrastructure
Chenyao Suo, Jianrong Wang, Yongjia Wang +3
MLIR (Multi-Level Intermediate Representation) compiler infrastructure provides an efficient framework for introducing a new abstraction level for programming languages and domain-…
Dependency-Aware Code Naturalness
Chen Yang, Junjie Chen, Jiajun Jiang +1
Code naturalness, which captures repetitiveness and predictability in programming languages, has proven valuable for various code-related tasks in software engineering. However, pr…
A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models
Junjie Yang, Jiajun Jiang, Zeyu Sun +1
Fairness has been a critical issue that affects the adoption of deep learning models in real practice. To improve model fairness, many existing methods have been proposed and evalu…
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…
Variable-Based Fault Localization via Enhanced Decision Tree
Jiajun Jiang, Yumeng Wang, Junjie Chen +2
Fault localization, aiming at localizing the root cause of the bug under repair, has been a longstanding research topic. Although many approaches have been proposed in the last dec…
Toward Understanding Deep Learning Framework Bugs
Junjie Chen, Yihua Liang, Qingchao Shen +2
DL frameworks are the basis of constructing all DL programs and models, and thus their bugs could lead to the unexpected behaviors of any DL program or model relying on them. Such…