arXiv subjects

Minwoo Jeong

Publications and source records attributed to Minwoo Jeong.

9 recordsLinked to original sources

arXiv2026

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

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, delaying feedback and increasing remediation costs. Existing static analyzers are often noisy and struggle with context-dependent or partially manifested vulnerabilities, while static large language model (LLM) reviewers are constrained by context windows and lack tool interaction. Agentic AI, which combines LLMs with code navigation, shows promise; however, its effectiveness for early-stage secure code review remains underexplored. We present AgenticSCR, an agentic secure code reviewer augmented with security-focused semantic memory that grounds reasoning in structured security knowledge to detect vulnerabilities before they fully manifest. AgenticSCR achieves at least 153% relative improvement in generating comments with correct localization, vulnerability type, and relevance over static LLM baseline, multi-agent reviewer, and SAST tools. In a shadow deployment, 54% of its comments were validated by security engineers for developer reporting, demonstrating practical utility while underscoring the difficulty of the task. These findings position the security-focused semantic memory as a promising direction for agentic secure code review, enabling early-stage vulnerability identification. Our approach builds an important step toward reliable localization, detection, and explanation in shift-left security practices

cs.CR↗

arXiv2026

Agentic Code Review in the Terminal: A Trajectory-Level Analysis of Behavior, Cost, and Human-Alignment

Agentic code review in terminal-based environments enables early feedback during local development before pull request creation. However, existing evaluations remain performance-centric and fail to capture the dynamic behaviors of repository-grounded agentic reviewers. Understanding these behaviors is critical for identifying how agentic reviewers succeed, fail, and incur hidden operational costs in practice. Then, we analyze the reviewers' behavior based on their trajectories. Our results show that agentic reviewers achieve higher review precision but incur substantial exploration and validation overhead, while successful reviews are associated with stronger planning and less downstream validation. These findings highlight the potential benefits of trajectory-aware and cost-sensitive evaluation of future agentic code review systems.

cs.SE↗

arXiv2026

Negative Resistance Caused by Intra-Loop Coupling in Virtual-Admittance-Based Grid-Forming Control

This paper addresses the harmonic instability problem of the virtual-admittance (VA)-based grid-forming control. It is revealed that the intra-loop coupling among the VA control, the inner-loop current control, and the voltage feedforward control results in an $s^2$-term in the equivalent output impedance of the inverter, which induces a negative-resistance property in the harmonic range. It is worth highlighting that this negative resistance is independent of the control delay. Consequently, this harmonic instability mechanism is fundamentally different from the extensively investigated cases in the literature, which are induced by the digital control delay of inverters. Then, a simple passivity-oriented damping control is proposed to mitigate the negative resistance arising from the intra-loop coupling. The method fully retains the well-established current controller and voltage feedforward, and does not require grid impedance information. Finally, experimental tests verify the theoretical findings and the effectiveness of the damping method.

eess.SY↗

arXiv2026

HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation

Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated review comments are ungrounded in the actual code -- poses a significant challenge to the adoption of LLMs in code review workflows. To address this, we explore effective and scalable methods for a hallucination detection in LLM-generated code review comments without the reference. In this work, we design HalluJudge that aims to assess the grounding of generated review comments based on the context alignment. HalluJudge includes four key strategies ranging from direct assessment to structured multi-branch reasoning (e.g., Tree-of-Thoughts). We conduct a comprehensive evaluation of these assessment strategies across Atlassian's enterprise-scale software projects to examine the effectiveness and cost-efficiency of HalluJudge. Furthermore, we analyze the alignment between HalluJudge's judgment and developer preference of the actual LLM-generated code review comments in the real-world production. Our results show that the hallucination assessment in HalluJudge is cost-effective with an F1 score of 0.85 and an average cost of $0.009. On average, 67% of the HalluJudge assessments are aligned with the developer preference of the actual LLM-generated review comments in the online production. Our results suggest that HalluJudge can serve as a practical safeguard to reduce developers' exposure to hallucinated comments, fostering trust in AI-assisted code reviews.

cs.SE↗

arXiv2026

RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian

Large Language Models (LLMs)-powered code review automation has the potential to transform code review workflows. Despite the advances of LLM-powered code review comment generation approaches, several practical challenges remain for designing enterprise-grade code review automation tools. In particular, this paper aims at answering the practical question: how can we design a review-guided, context-aware, quality-checked code review comment generation without fine-tuning? In this paper, we present RovoDev Code Reviewer, an enterprise-grade LLM-based code review automation tool designed and deployed at scale within Atlassian's development ecosystem with seamless integration into Atlassian's Bitbucket. Through the offline, online, user feedback evaluations over a one-year period, we conclude that RovoDev Code Reviewer is effective in generating code review comments that could lead to code resolution for 38.70% (i.e., comments that triggered code changes in the subsequent commits); and offers the promise of accelerating feedback cycles (i.e., decreasing the PR cycle time by 30.8%), alleviating reviewer workload (i.e., reducing the number of human-written comments by 35.6%), and improving overall software quality (i.e., finding errors with actionable suggestions).

cs.SE↗

arXiv2025

AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Models

The growing complexity of urban mobility systems has made traffic simulation indispensable for evidence-based transportation planning and policy evaluation. However, despite the analytical capabilities of platforms such as the Simulation of Urban MObility (SUMO), their application remains largely confined to domain experts. Developing realistic simulation scenarios requires expertise in network construction, origin-destination modeling, and parameter configuration for policy experimentation, creating substantial barriers for non-expert users such as policymakers, urban planners, and city officials. Moreover, the requests expressed by these users are often incomplete and abstract-typically articulated as high-level objectives, which are not well aligned with the imperative, sequential workflows employed in existing language-model-based simulation frameworks. To address these challenges, this study proposes AgentSUMO, an agentic framework for interactive simulation scenario generation via large language models. AgentSUMO departs from imperative, command-driven execution by introducing an adaptive reasoning layer that interprets user intents, assesses task complexity, infers missing parameters, and formulates executable simulation plans. The framework is structured around two complementary components, the Interactive Planning Protocol, which governs reasoning and user interaction, and the Model Context Protocol, which manages standardized communication and orchestration among simulation tools. Through this design, AgentSUMO converts abstract policy objectives into executable simulation scenarios. Experiments on urban networks in Seoul and Manhattan demonstrate that the agentic workflow achieves substantial improvements in traffic flow metrics while maintaining accessibility for non-expert users, successfully bridging the gap between policy goals and executable simulation workflows.

cs.HC↗

arXiv2025

What Types of Code Review Comments Do Developers Most Frequently Resolve?

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. Understanding what types of generated review comments are likely to trigger code changes is crucial for identifying those that are actionable. In this paper, we set out to investigate (1) the types of review comments written by humans and LLMs, and (2) the types of generated comments that are most frequently resolved by developers. To do so, we developed an LLM-as-a-Judge to automatically classify review comments based on our own taxonomy of five categories. Our empirical study confirms that (1) the LLM reviewer and human reviewers exhibit distinct strengths and weaknesses depending on the project context, and (2) readability, bugs, and maintainability-related comments had higher resolution rates than those focused on code design. These results suggest that a substantial proportion of LLM-generated comments are actionable and can be resolved by developers. Our work highlights the complementarity between LLM and human reviewers and offers suggestions to improve the practical effectiveness of LLM-powered code review tools.

cs.SE↗

arXiv2025

Integrating Urban Air Mobility with Highway Infrastructure: A Strategic Approach for Vertiport Location Selection in the Seoul Metropolitan Area

This study focuses on identifying suitable locations for highway-transfer Vertiports to integrate Urban Air Mobility (UAM) with existing highway infrastructure. UAM offers an effective solution for enhancing transportation accessibility in the Seoul Metropolitan Area, where conventional transportation often struggle to connect suburban employment zones such as industrial parks. By integrating UAM with ground transportation at highway facilities, an efficient connectivity solution can be achieved for regions with limited transportation options. Our proposed methodology for determining the suitable Vertiport locations utilizes data such as geographic information, origin-destination volume, and travel time. Vertiport candidates are evaluated and selected based on criteria including location desirability, combined transportation accessibility and transportation demand. Applying this methodology to the Seoul metropolitan area, we identify 56 suitable Vertiport locations out of 148 candidates. The proposed methodology offers a strategic approach for the selection of highway-transfer Vertiport locations, enhancing UAM integration with existing transportation systems. Our study provides valuable insights for urban planners and policymakers, with recommendations for future research to include real-time environmental data and to explore the impact of Mobility-as-a-Service on UAM operations.

cs.CY↗

arXiv2022

Connecting Low-Loss Subspace for Personalized Federated Learning

Due to the curse of statistical heterogeneity across clients, adopting a personalized federated learning method has become an essential choice for the successful deployment of federated learning-based services. Among diverse branches of personalization techniques, a model mixture-based personalization method is preferred as each client has their own personalized model as a result of federated learning. It usually requires a local model and a federated model, but this approach is either limited to partial parameter exchange or requires additional local updates, each of which is helpless to novel clients and burdensome to the client's computational capacity. As the existence of a connected subspace containing diverse low-loss solutions between two or more independent deep networks has been discovered, we combined this interesting property with the model mixture-based personalized federated learning method for improved performance of personalization. We proposed SuPerFed, a personalized federated learning method that induces an explicit connection between the optima of the local and the federated model in weight space for boosting each other. Through extensive experiments on several benchmark datasets, we demonstrated that our method achieves consistent gains in both personalization performance and robustness to problematic scenarios possible in realistic services.

cs.LG↗