15 citations · 15 across the 9 of their papers we have counts for
11 papers
Adaptive Code Revision Attacks on AI Pull Request Reviewers
Jingzhi Gong, Jie M. Zhang, Gunel Jahangirova +1
Pull-request review protects software before new code reaches users, helping prevent vulnerabilities that could expose users to attacks. AI agents increasingly perform these review…
COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation
Jingzhi Gong, Jie M. Zhang, Gunel Jahangirova +3
Code generation systems make each LLM call with a model, a prompt, and decoding settings. However, existing optimization methods usually tune only part of these choices or use one…
An Empirical Study of Downstream Adaptation for Agent Skills
Xinjian Wu, Jingzhi Gong, Gunel Jahangirova +2
As Large Language Model (LLM) agents become integral to modern software systems, ``skills'' have emerged as a novel unit of software reuse, enabling developers to package workflows…
How Does Chunking Affect Retrieval-Augmented Code Completion? A Controlled Empirical Study
Xinjian Wu, Jingzhi Gong, Gunel Jahangirova +1
Retrieval-augmented generation (RAG) pipelines for code completion rely on chunking to segment source files into retrievable units, yet chunking strategies are typically adopted wi…
Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
Zezhen Xiang, Jingzhi Gong, Tao Chen
Modern configurable software systems need to learn models that correlate configuration and performance. However, when the system operates in dynamic environments, the workload vari…
Learning Software Bug Reports: A Systematic Literature Review
Guoming Long, Jingzhi Gong, Hui Fang +1
The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aim…
Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning
Pengzhou Chen, Jingzhi Gong, Tao Chen
To ease the expensive measurements during configuration tuning, it is natural to build a surrogate model as the replacement of the system, and thereby the configuration performance…
Dividable Configuration Performance Learning
Jingzhi Gong, Tao Chen, Rami Bahsoon
Machine/deep learning models have been widely adopted for predicting the configuration performance of software systems. However, a crucial yet unaddressed challenge is how to cater…
Pushing the Boundary: Specialising Deep Configuration Performance Learning
Jingzhi Gong
Software systems often have numerous configuration options that can be adjusted to meet different performance requirements. However, understanding the combined impact of these opti…
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
Jingzhi Gong, Tao Chen
Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern softwar…
Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning
Jingzhi Gong, Tao Chen
Learning and predicting the performance of given software configurations are of high importance to many software engineering activities. While configurable software systems will al…