Serverless computing has emerged as a pivotal cloud deployment paradigm due to its efficient utilization of on-demand resources. However, the persistent cold start problem continues to hinder system performance. Current strategies often employ oversimplified preloading/unloading mechanisms that inadequately exploit inv...
Cheryl Lee, Zi-Shan Su, Zhouruixing Zhu et al.· 0 citations
Interactive webpages underpin modern applications, but reproducing both their visual appearance and multi-step behavior remains challenging for multimodal large language models (MLLMs). Scalable training data must capture user actions, state transitions, and expected outcomes, while real-world webpage code is often ent...
Jingyu Xiao, Da-Sen Dai, Yuxuan Wan et al.· 0 citations
Large Language Model (LLM) services expose a large configuration space that critically affects performance, yet tuning these configurations on hardware (e.g., NVIDIA GPUs) is prohibitively expensive. Simulation offers a scalable alternative, but state-of-the-art simulators suffer from a fundamental software engineering...
Yu-Jie Huang, Jia-Zhen Gu, Zhihan Jiang et al.· Proceedings of the 41st IEEE...· 0 citations
Browser extensions can pose significant privacy risks to end users due to their complex permission mechanisms and high privilege levels. Modern websites increasingly embed sensitive personal data within HTML elements, making such threats even more insidious. Although prior studies have examined browser extension privac...
Zi-Lun Wang, Ze-Yang Zhuang, Ming-Xue Zhang et al.· Proceedings of the 41st IEEE...· 0 citations
Material generation should produce not only an appearance, but also the rules that construct it. We introduce MatLoom, a compact, layer-oriented language for text-to-material generation with pretrained language models. Each program composes alpha-masked layers whose shared spatial expressions define coverage and physic...
This work proposes a framework for quality-aware preference learning that guides LLMs toward generating criteria-compliant code and introduces a hybrid optimization objective that combines ranking loss with language modeling loss and KL divergence to enable effective comparative optimization.
Liang Lu, Yuan Jiang, Christoph Treude et al.· 2 citations
This work proposes AutoScope, a novel framework that leverages static code knowledge to enable structurally safe span-level sampling and introduces the concept of span-level sampling, a fine-grained paradigm that retains only essential spans rather than full traces.
Yu-Lun Wu, Guangba Yu, Zhihan Jiang et al.· ACM Transactions on Software...· 0 citations
This work proposes ComUICoder, a component-based UI code generation framework that emphasizes semantic-aware segmentation, code reuse, and fine-grained refinement and significantly improves overall generation quality and code reusability on complex multipage websites.
Jingyu Xiao, Jiantong Qin, Shuo-Qiu Li et al.· Proceedings of the 32nd ACM...· 1 citation
SeaLog is presented, an accurate, lightweight, and adaptive log-based anomaly detection framework that synergistically integrates the efficiency of traditional data-driven methods with the zero/few-shot learning ability of large language models (LLMs).
Jinyang Liu, Jun-Jie Huang, Zhihan Jiang et al.· ACM Transactions on Software...· 0 citations
Results show that combining layout compaction, adaptive configuration, and instruction-aware pruning can make multimodal code understanding more efficient, and propose CodeShrink, an adaptive visual compression framework with three components.
Wenxin Tang, Jingyu Xiao, Zhen-Yu Liu et al.· arXiv.org· 1 citation
VisualRepair is proposed, an MLLM-based framework for visual software issue repair comprising two core modules: Image Type-aware Tool Calling (ITTC), which classifies input images and dynamically invokes a tailored tool-calling chain for robust visual interpretation, and Dynamic Test-time Region Focusing (DTRF), which...
This work proposes ComUICoder, a component-based UI code generation framework that emphasizes semantic-aware segmentation, code reuse, and fine-grained refinement and significantly improves overall generation quality and code reusability on complex multipage websites.
Jingyu Xiao, Jiantong Qin, ShuoQiu Li et al.· Proceedings of the 32nd ACM...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
