activity
20232026
most citedLLM4SR: A Survey on Large Language Models for Scientific Research

5 citations · 9 across the 13 of their papers we have counts for

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9 papers · 1 filter

cs.SE2026

TileCas: Cascading Kernel Development Across Programming Abstractions

Xinyu Xiao, Zihe Song, Dezhi Ran +2

Programming abstractions have traditionally been designed for humans, but code generation is increasingly performed by models and autonomous agents. We investigate abstractions for…

cs.SE2026

BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services

Yuzhe Guo, Mengzhou Wu, Yuan Cao +4

Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise c…

cs.SE2026

Understanding Automated Web GUI Testing: An Empirical Study Across Exploration Strategies and State Abstractions

Chenxu Liu, Wei Yang, Ying Zhang +1

Automated web GUI testing (AWGT) relies on exploration strategies that exercise web applications through GUI actions to maximize code coverage, spanning traditional model-based, re…

cs.SE2026

Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging

Ziyue Hua, Tianyu Chen, Yeyun Gong +8

Automatic unit test (UT) generation is essential for software quality assurance, but existing approaches--including symbolic execution, search-based approaches, and recent LLM-base…

cs.SE2025

From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents

Dezhi Ran, Zhi Gong, Yuzhe Guo +10

While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooke…

cs.SE2025

AppForge: From Assistant to Independent Developer -- Are GPTs Ready for Software Development?

Dezhi Ran, Yuan Cao, Mengzhou Wu +10

Large language models (LLMs) have demonstrated remarkable capability in function-level code generation tasks. Unlike isolated functions, real-world applications demand reasoning ov…

cs.SE2025

Beyond Pass or Fail: Multi-Dimensional Benchmarking of Foundation Models for Goal-based Mobile UI Navigation

Dezhi Ran, Mengzhou Wu, Hao Yu +15

Recent advances of foundation models (FMs) have made navigating mobile applications (apps) based on high-level goal instructions within reach, with significant industrial applicati…

cs.SE2024★ 1 cited

Skill-Adaptive Imitation Learning for UI Test Reuse

Mengzhou Wu, Hao Wang, Jun Ren +7

To alleviate the substantial cost of manually crafting user interface (UI) test cases, UI test migration aims to automatically generate test cases for a target mobile application (…

cs.SE2024★ 1 cited

Foundation Model Engineering: Engineering Foundation Models Just as Engineering Software

Dezhi Ran, Mengzhou Wu, Wei Yang +1

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs ma…