38 citations · 88 across the 35 of their papers we have counts for
37 papers
Towards Risk-free AI Agent Deployment
Yintong Huo, Rangeet Pan, Abhik Roychoudhury
LLM-based agents are rapidly moving from research prototypes into the core business processes of organizations, but these agents pose deployment risks to security, compliance, and…
ExplainBench: Evaluating Code Explanations from Agents
Zhiyuan Pan, Sungmin Kang, Imam Nur Bani Yusuf +1
Large Language Model (LLM) agents have seen rapid adoption in software engineering. As agents take a greater role in the actual generation of code, they are making larger changes,…
Code-Augur: Agentic Vulnerability Detection via Specification Inference
Zhengxiong Luo, Mehtab Zafar, Dylan Wolff +1
The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM agents are uncovering critic…
Skills for the future software profession: beyond agentic AI!
Abhik Roychoudhury, Sungmin Kang, Baishakhi Ray
As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers? To identify where software engineer…
Code Review Agent Benchmark
Yuntong Zhang, Zhiyuan Pan, Imam Nur Bani Yusuf +3
Software engineering agents have shown significant promise in writing code. As AI agents permeate code writing, and generate huge volumes of code automatically -- the matter of cod…
VeriGrey: Greybox Agent Validation
Yuntong Zhang, Sungmin Kang, Ruijie Meng +2
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end. In the front end, it conducts autonomous decis…
Automated Lemma Discovery in Agentic Program Verification
Huan Zhao, Haoxin Tu, Zhengyao Liu +2
Deductive verification provides strong correctness guarantees for code by extracting verification conditions (VCs) and writing formal proofs for them. The expertise-intensive task…
SE Journals in 2036: Looking Back at the Future We Need to Have
Tim Menzies, Paris Avgeriou, Robert Feldt +5
In 2025, SE publishing faces an existential crisis of scalability. As our communities swell globally and integrate fast-moving methodologies like LLMs, traditional peer-review prac…
Agentic Verification of Software Systems
Haoxin Tu, Huan Zhao, Yahui Song +3
Automatically generated code is gaining traction recently, owing to the prevalence of Large Language Models (LLMs). Further, the AlphaProof initiative has demonstrated the possibil…
Agentic AI for Software: thoughts from Software Engineering community
Abhik Roychoudhury
AI agents have recently shown significant promise in software engineering. Much public attention has been transfixed on the topic of code generation from Large Language Models (LLM…
AutoCodeSherpa: Symbolic Explanations in AI Coding Agents
Sungmin Kang, Haifeng Ruan, Abhik Roychoudhury
Large language model (LLM) agents integrate external tools with one or more LLMs to accomplish specific tasks. Agents have rapidly been adopted by developers, and they are starting…
Unified Software Engineering Agent as AI Software Engineer
Leonhard Applis, Yuntong Zhang, Shanchao Liang +3
The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities…
Agent That Debugs: Dynamic State-Guided Vulnerability Repair
Zhengyao Liu, Yunlong Ma, Jingxuan Xu +4
In recent years, more vulnerabilities have been discovered every day, while manual vulnerability repair requires specialized knowledge and is time-consuming. As a result, many dete…
Concurrency Testing in the Linux Kernel via eBPF
Jiacheng Xu, Dylan Wolff, Xing Yi Han +2
Concurrency is indispensable for modern software systems to meet performance and scalability demands, yet concurrency bugs remain notoriously difficult to detect and reproduce. Con…
Will AI replace Software Engineers? Do not hold your breath
Abhik Roychoudhury, Andreas Zeller
Artificial Intelligence (AI) technology such as Large Language Models (LLMs) have become extremely popular in creating code. This has led to the conjecture that future software job…
Computation Tree Logic Guided Program Repair
Yu Liu, Yahui Song, Martin Mirchev +1
Temporal logics like Computation Tree Logic (CTL) have been widely used as expressive formalisms to capture rich behavioral specifications. CTL can express properties such as reach…
Agentic AI Software Engineers: Programming with Trust
Abhik Roychoudhury, Corina Pasareanu, Michael Pradel +1
Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (A…
AFLNet Five Years Later: On Coverage-Guided Protocol Fuzzing
Ruijie Meng, Van-Thuan Pham, Marcel Böhme +1
Protocol implementations are stateful which makes them difficult to test: Sending the same test input message twice might yield a different response every time. Our proposal to con…
Large Language Model assisted Hybrid Fuzzing
Ruijie Meng, Gregory J. Duck, Abhik Roychoudhury
Greybox fuzzing is one of the most popular methods for detecting software vulnerabilities, which conducts a biased random search within the program input space. To enhance its effe…
Fixing Security Vulnerabilities with AI in OSS-Fuzz
Yuntong Zhang, Jiawei Wang, Dominic Berzin +5
Critical open source software systems undergo significant validation in the form of lengthy fuzz campaigns. The fuzz campaigns typically conduct a biased random search over the dom…
Assured Automatic Programming via Large Language Models
Martin Mirchev, Andreea Costea, Abhishek Kr Singh +1
With the advent of AI-based coding engines, it is possible to convert natural language requirements to executable code in standard programming languages. However, AI-generated code…
Whose fault is it anyway? SILC: Safe Integration of LLM-Generated Code
Peisen Lin, Yuntong Zhang, Andreea Costea +1
In modern software development, multiple software components, often sourced from different contributors, including AI assistants, are combined to create a cohesive system. Although…
SpecRover: Code Intent Extraction via LLMs
Haifeng Ruan, Yuntong Zhang, Abhik Roychoudhury
Autonomous program improvement typically involves automatically producing bug fixes and feature additions. Such program improvement can be accomplished by a combination of large la…
Automatic Programming: Large Language Models and Beyond
Michael R. Lyu, Baishakhi Ray, Abhik Roychoudhury +2
Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically g…