38 citations · 88 across the 35 of their papers we have counts for
29 papers · 1 filter
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,…
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…
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…
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…
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…
Codexity: Secure AI-assisted Code Generation
Sung Yong Kim, Zhiyu Fan, Yannic Noller +1
Despite the impressive performance of Large Language Models (LLMs) in software development activities, recent studies show the concern of introducing vulnerabilities into software…
Program Environment Fuzzing
Ruijie Meng, Gregory J. Duck, Abhik Roychoudhury
Computer programs are not executed in isolation, but rather interact with the execution environment which drives the program behaviors. Software validation methods thus need to cap…
AutoCodeRover: Autonomous Program Improvement
Yuntong Zhang, Haifeng Ruan, Zhiyu Fan +1
Researchers have made significant progress in automating the software development process in the past decades. Recent progress in Large Language Models (LLMs) has significantly imp…
Software Engineering Educational Experience in Building an Intelligent Tutoring System
Zhiyu Fan, Yannic Noller, Ashish Dandekar +1
The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, part…