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20242026
most citedSWE-Bench+: Enhanced Coding Benchmark for LLMs

5 citations · 6 across the 11 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.SE2026

Developer Experiences with Cursor AI-native IDE

Haoran Xue, Gias Uddin, Song Wang

AI-powered integrated development environments (IDEs) are increasingly positioned as practical tools for software development, integrating AI assistance into everyday activities su…

cs.CR2026

Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities

Fares Trad, Simin Chen, Hung Viet Pham +2

Software agents with Large Language Models (LLMs) are designed for Automated Program Repair (APR) tasks, raising the possibility that, in the near future, APR agents will fix bugs…

cs.SE2026

Impossible to hide secret ...: Uncovering Security and Privacy Issues in LLM-native IDEs

Mostafijur Rahman Akhond, Md Afif Al Mamun, Gias Uddin +1

LLM-native IDEs (Integrated Development Environments), aka LIDEs, are designed from the ground up to work with Large Language Models (LLMs). LIDEs have found remarkable success in…

cs.SE2026

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

Sanjeepan Sivapiran, Gias Uddin

Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards. While LLM alignment techniques are studi…

cs.SE2026

BLAgent: Agentic RAG for File-Level Bug Localization

Md Afif Al Mamun, Gias Uddin

Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause…

cs.SE2026

ABTest: Behavior-Driven Testing for AI Coding Agents

Wuyang Dai, Moses Openja, Hung Viet Pham +3

AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood.…

cs.SE2026★ 1 cited

Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI

Ruixin Zhang, Wuyang Dai, Hung Viet Pham +3

The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and…