9 citations · 20 across the 18 of their papers we have counts for
19 papers
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…
Benchmarking and Evaluating VLMs for Software Architecture Diagram Understanding
Shuyin Ouyang, Jie M. Zhang, Jingzhi Gong +7
Software architecture diagrams are important design artifacts for communicating system structure, behavior, and data organization throughout the software development lifecycle. Alt…
LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops
Ravin Ravi, Dylan Bradshaw, Stefano Ruberto +2
Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Resear…
Revisiting "Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion": A Critical Review and Implications on DNN Coverage Testing
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
We present a critical review of Neural Coverage (NLC), a state-of-the-art DNN coverage criterion by Yuan et al. at ICSE 2023. While NLC proposes to satisfy eight design requirement…