works on

From the 1 of 20 linked papers with an AI index.

activity
20242026
most citedSecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios

2 citations · 2 across the 12 of their papers we have counts for

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14 papers · 1 filter

cs.SE2026

AgentExecutor: Partial Code Execution via Agentic Context Generation

Junkai Chen, Chengran Yang, Xing Hu +3

Executing code snippets is essential for dynamic program analysis, but it remains challenging to execute an arbitrary code snippet due to issues like missing context and incomplete…

cs.SE2026

SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements

Pengyu Xue, He Yang Yuan, Xin Wang +6

The paper introduces SWE-NFI, a benchmark that assesses how coding agents can make non-functional, behavior-preserving improvements to Python code, using real pull‑request tasks an…

cs.SE20262 cited

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios

Junkai Chen, Huihui Huang, Yunbo Lyu +10

Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benc…

cs.SE2026

An Empirical Study of Speculative Decoding on Software Engineering Tasks

Yijia Li, Junkai Chen, Xing Hu +1

Large Language Models (LLMs) have become widely used for Software Engineering (SE) tasks, spanning from function-level code generation to complex repository-level workflows. Howeve…

cs.SE2026

Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild

Yue Liu, Ratnadira Widyasari, Yanjie Zhao +3

AI coding assistants are now widely used in software development. Software developers increasingly integrate AI-generated code into their codebases to improve productivity. Prior s…

cs.SE2026

AgentSZZ: Teaching the LLM Agent to Play Detective with Bug-Inducing Commits

Yunbo Lyu, Jieke Shi, Hong Jin Kang +8

The SZZ algorithm is the dominant technique for identifying bug-inducing commits and underpins many software engineering tasks, such as defect prediction and vulnerability analysis…