Tootfinder

Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality
Suzhen Zhong, Ying Zou, Bram Adams
https://arxiv.org/abs/2509.10402 https://

Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality
Large Language Models (LLMs) are becoming integral to modern software development workflows, assisting developers with code generation, API explanation, and iterative problem-solving through natural language conversations. Despite widespread adoption, there is limited understanding of how developers interact with LLMs in practice and how these conversational dynamics influence task outcomes, code quality, and software engineering workflows. To address this, we leverage CodeChat, a large dataset…

Model-Assisted and Human-Guided: Perceptions and Practices of Software Professionals Using LLMs for Coding
Italo Santos, Cleyton Magalhaes, Ronnie de Souza Santos
https://arxiv.org/abs/2510.09058

Model-Assisted and Human-Guided: Perceptions and Practices of Software Professionals Using LLMs for Coding
Large Language Models have quickly become a central component of modern software development workflows, and software practitioners are increasingly integrating LLMs into various stages of the software development lifecycle. Despite the growing presence of LLMs, there is still a limited understanding of how these tools are actually used in practice and how professionals perceive their benefits and limitations. This paper presents preliminary findings from a global survey of 131 software practiti…

A Humanoid Social Robot as a Teaching Assistant in the Classroom
Thomas Sievers
https://arxiv.org/abs/2508.05646 https://arxiv.org/pdf/2508.05646

A Humanoid Social Robot as a Teaching Assistant in the Classroom
Although innovation and the support of new technologies are much needed to ease the burden on the education system, social robots in schools to help teachers with educational tasks are rare. Child-Robot Interaction (CRI) could support teachers and add an embodied social component to modern multi-modal and multi-sensory learning environments already in use. The social robot Pepper, connected to the Large Language Model (LLM) ChatGPT, was used in a high school classroom to teach new learning cont…

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Vasudha Yanuganti, Ishaan Puri, Swapnil Chhatre, Mantinder Singh, Ashok Jallepalli, Hritvik Shrivastava, Pradeep Kumar Sharma
https://arxiv.org/abs/2510.08850

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Modern codebases make it hard for developers and AI coding assistants to find the right source files when answering questions like "How does this feature work?" or "Where was the bug introduced?" Traditional code search (keyword or IR based) often misses semantic context and cross file links, while large language models (LLMs) understand natural language but lack repository specific detail. We present a method for file path retrieval that fine tunes a strong LLM (Qwen3-8B) with QLoRA and Unslot…

REMINDER: Working With Tainted Legacies (virtual NeMLA panel)
https://ift.tt/fVI7RB8
updated: Monday, August 11, 2025 - 7:48amfull name / name of organization: Northeast Modern Language…
via Input 4 RELCFP

Code Agent can be an End-to-end System Hacker: Benchmarking Real-world Threats of Computer-use Agent
Weidi Luo, Qiming Zhang, Tianyu Lu, Xiaogeng Liu, Bin Hu, Hung-Chun Chiu, Siyuan Ma, Yizhe Zhang, Xusheng Xiao, Yinzhi Cao, Zhen Xiang, Chaowei Xiao
https://arxiv.org/abs/2510.06607

Code Agent can be an End-to-end System Hacker: Benchmarking Real-world Threats of Computer-use Agent
Computer-use agent (CUA) frameworks, powered by large language models (LLMs) or multimodal LLMs (MLLMs), are rapidly maturing as assistants that can perceive context, reason, and act directly within software environments. Among their most critical applications is operating system (OS) control. As CUAs in the OS domain become increasingly embedded in daily operations, it is imperative to examine their real-world security implications, specifically whether CUAs can be misused to perform realistic…

Reasoning-enhanced Query Understanding through Decomposition and Interpretation
Yunfei Zhong, Jun Yang, Yixing Fan, Jiafeng Guo, Lixin Su, Maarten de Rijke, Ruqing Zhang, Dawei Yin, Xueqi Cheng
https://arxiv.org/abs/2509.06544

Reasoning-enhanced Query Understanding through Decomposition and Interpretation
Accurate inference of user intent is crucial for enhancing document retrieval in modern search engines. While large language models (LLMs) have made significant strides in this area, their effectiveness has predominantly been assessed with short, keyword-based queries. As AI-driven search evolves, long-form queries with intricate intents are becoming more prevalent, yet they remain underexplored in the context of LLM-based query understanding (QU). To bridge this gap, we introduce ReDI: a Reaso…

VoltanaLLM: Feedback-Driven Frequency Control and State-Space Routing for Energy-Efficient LLM Serving
Jiahuan Yu (University of Illinois Urbana-Champaign), Aryan Taneja (University of Illinois Urbana-Champaign), Junfeng Lin (Tsinghua University), Minjia Zhang (University of Illinois Urbana-Champaign)
https://arxiv.org/abs/2509.04827

VoltanaLLM: Feedback-Driven Frequency Control and State-Space Routing for Energy-Efficient LLM Serving
Modern Large Language Model (LLM) serving systems increasingly support interactive applications, like real-time chat assistants, code generation tools, and agentic workflows. However, the soaring energy cost of LLM inference presents a growing challenge for sustainable and cost-effective deployment. This paper introduces VoltanaLLM, a system for SLO-aware, energy-efficient LLM serving, built from a control theory perspective. VoltanaLLM co-designs frequency scaling and request routing in emergi…

Takedown: How It's Done in Modern Coding Agent Exploits
Eunkyu Lee, Donghyeon Kim, Wonyoung Kim, Insu Yun
https://arxiv.org/abs/2509.24240 https://arxi…

Takedown: How It's Done in Modern Coding Agent Exploits
Coding agents, which are LLM-driven agents specialized in software development, have become increasingly prevalent in modern programming environments. Unlike traditional AI coding assistants, which offer simple code completion and suggestions, modern coding agents tackle more complex tasks with greater autonomy, such as generating entire programs from natural language instructions. To enable such capabilities, modern coding agents incorporate extensive functionalities, which in turn raise signi…

FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration
Victor May, Diganta Misra, Yanqi Luo, Anjali Sridhar, Justine Gehring, Silvio Soares Ribeiro Junior
https://arxiv.org/abs/2510.04852

FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration
AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative-but their effectiveness has not been systematically evaluated. In this paper, …