2 citations · 4 across the 7 of their papers we have counts for
7 papers
Security and Privacy Taxonomy Generation from Mobile App Reviews
Moghis Fereidouni, Vinaik Chhetri, Krishna Upadhyay +2
Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are hand-crafted and cannot keep…
A Large-Scale Study on the Development and Issues of Multi-Agent AI Systems
Daniel Liu, Krishna Upadhyay, Vinaik Chhetri +2
The rapid emergence of multi-agent AI systems (MAS), including LangChain, CrewAI, and AutoGen, has shaped how large language model (LLM) applications are developed and orchestrated…
Understanding Robustness of Model Editing in Code LLMs
Vinaik Chhetri, Moghis Fereidouni, A. B Siddique +1
Large language models (LLMs) for code are increasingly used in software development, but they remain static after pretraining while APIs and software libraries continue to evolve.…
A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
Vinaik Chhetri, Yousaf Reza, Moghis Fereidouni +3
Modern recommendation systems typically follow two complementary paradigms: collaborative filtering, which models long-term user preferences from historical interactions, and conve…
What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps
Vinaik Chhetri, Krishna Upadhyay, A. B. Siddique +1
Artificial Intelligence (AI)-powered features have rapidly proliferated across mobile apps in various domains, including productivity, education, entertainment, and creativity. How…
Analyzing the Evolution and Maintenance of Quantum Software Repositories
Krishna Upadhyay, Vinaik Chhetri, A. B. Siddique +1
Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of ma…
MobileConvRec: A Conversational Dataset for Mobile Apps Recommendations
Srijata Maji, Moghis Fereidouni, Vinaik Chhetri +2
Existing recommendation systems have focused on two paradigms: 1- historical user-item interaction-based recommendations and 2- conversational recommendations. Conversational recom…