114 citations · 129 across the 14 of their papers we have counts for
6 papers · 1 filter
Taming the Drift: Context-aware Repair of Dockerfile Drift during Software Evolution
Chengjie Wang, Jingzheng Wu, Xiang Ling +2
Docker is widely used to create reproducible build environments, but Dockerfile drift, the divergence between a Dockerfile and its evolving source code, can cause CI/CD builds to f…
Correct Code, Vulnerable Dependencies: A Large Scale Measurement Study of LLM-Specified Library Versions
Chengjie Wang, Jingzheng Wu, Xiang Ling +2
Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third-party library (TPL) imports annotated w…
A Large Scale Empirical Analysis on the Adherence Gap between Standards and Tools in SBOM
Chengjie Wang, Jingzheng Wu, Hao Lyu +4
A Software Bill of Materials (SBOM) is a machine-readable artifact that systematically organizes software information, enhancing supply chain transparency and security. To facilita…
Feature Slice Matching for Precise Bug Detection
Ke Ma, Jianjun Huang, Wei You +4
Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the no…
The Seeds of the FUTURE Sprout from History: Fuzzing for Unveiling Vulnerabilities in Prospective Deep-Learning Libraries
Zhiyuan Li, Jingzheng Wu, Xiang Ling +3
The widespread application of large language models (LLMs) underscores the importance of deep learning (DL) technologies that rely on foundational DL libraries such as PyTorch and…
When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?
Yuxiao Chen, Jingzheng Wu, Xiang Ling +4
In recent years, large language models (LLMs) have demonstrated substantial potential in addressing automatic program repair (APR) tasks. However, the current evaluation of these m…