138 citations · 477 across the 59 of their papers we have counts for
61 papers
SkillGate: Cost Efficient Runtime Malicious Skill File Detection in Coding Agents
Rui Yang, Michael Fu, Kla Tantithamthavorn +2
Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing domain-specific skill files to tailor age…
Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild
Hong Yi Lin, Mingzhao Liang, Patanamon Thongtanunam +1
Agentic code review, where autonomous agents provide code review comments on pull requests, is increasingly integrated into development workflows, yet there is limited empirical ev…
AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3
Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, de…
When AI Takes the Wheel: Security Analysis of Framework-Constrained Program Generation
Yue Liu, Zhenchang Xing, Shidong Pan +1
In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex framework-constrained software systems based on t…
What Types of Code Review Comments Do Developers Most Frequently Resolve?
Saul Goldman, Hong Yi Lin, Jirat Pasuksmit +11
Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes.…
DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3
Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an i…
AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3
Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces…
Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs
Fanyu Wang, Chetan Arora, Yonghui Liu +5
Acceptance criteria (ACs) play a critical role in software development by clearly defining the conditions under which a software feature satisfies stakeholder expectations. However…
On the Evaluation of Large Language Models in Multilingual Vulnerability Repair
Dong wang, Junji Yu, Honglin Shu +4
Various Deep Learning-based approaches with pre-trained language models have been proposed for automatically repairing software vulnerabilities. However, these approaches are limit…
SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems
Wenliang Shan, Michael Fu, Rui Yang +1
Safety alignment is critical for LLM-powered systems. While recent LLM-powered guardrail approaches such as LlamaGuard achieve high detection accuracy of unsafe inputs written in E…
From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry
Chetan Arora, Fanyu Wang, Chakkrit Tantithamthavorn +2
Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constra…
Human-In-The-Loop Software Development Agents: Challenges and Future Directions
Jirat Pasuksmit, Wannita Takerngsaksiri, Patanamon Thongtanunam +8
Multi-agent LLM-driven systems for software development are rapidly gaining traction, offering new opportunities to enhance productivity. At Atlassian, we deployed Human-in-the-Loo…
Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities
Honglin Shu, Michael Fu, Junji Yu +4
Various deep learning-based approaches utilizing pre-trained language models (PLMs) have been proposed for automated vulnerability detection. With recent advancements in large lang…
A Preliminary Study of Large Language Models for Multilingual Vulnerability Detection
Junji Yu, Honglin Shu, Michael Fu +4
Deep learning-based approaches, particularly those leveraging pre-trained language models (PLMs), have shown promise in automated software vulnerability detection. However, existin…
Blended PC Peer Review Model: Process and Reflection
Chakkrit Tantithamthavorn, Nicole Novielli, Ayushi Rastogi +2
The academic peer review system is under increasing pressure due to a growing volume of submissions and a limited pool of available reviewers, resulting in delayed decisions and an…
RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3
Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retri…
Requirements-Driven Automated Software Testing: A Systematic Review
Fanyu Wang, Chetan Arora, Chakkrit Tantithamthavorn +2
Automated software testing has significant potential to enhance efficiency and reliability within software development processes. However, its broader adoption faces considerable c…
Comparing Human and LLM Generated Code: The Jury is Still Out!
Sherlock A. Licorish, Ansh Bajpai, Chetan Arora +2
Much is promised in relation to AI-supported software development. However, there has been limited evaluation effort in the research domain aimed at validating the true utility of…
Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Micheal Fu +3
Software engineers spend a significant amount of time reading code during the software development process, especially in the age of large language models (LLMs) that can automatic…
Protect Your Secrets: Understanding and Measuring Data Exposure in VSCode Extensions
Yue Liu, Chakkrit Tantithamthavorn, Li Li
Recent years have witnessed the emerging trend of extensions in modern Integrated Development Environments (IDEs) like Visual Studio Code (VSCode) that significantly enhance develo…
MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems
Aaron Guoxiang Guo, Aldeida Aleti, Neelofar Neelofar +3
With the widespread application of LLM-based dialogue systems in daily life, quality assurance has become more important than ever. Recent research has successfully introduced meth…
Human-In-the-Loop Software Development Agents
Wannita Takerngsaksiri, Jirat Pasuksmit, Patanamon Thongtanunam +7
Recently, Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks (e.g., from a given i…
Code Ownership: The Principles, Differences, and Their Associations with Software Quality
Patanamon Thongtanunam, Chakkrit Tantithamthavorn
Code ownership -- an approximation of the degree of ownership of a software component -- is one of the important software measures used in quality improvement plans. However, prior…
What do AI/ML practitioners think about AI/ML bias?
Aastha Pant, Rashina Hoda, Burak Turhan +1
AI leaders and companies have much to offer to AI/ML practitioners to support them in addressing and mitigating biases in the AI/ML systems they develop. AI/ML practitioners need t…