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20232026
most citedGenerating Java Methods: An Empirical Assessment of Four AI-Based Code Assistants

24 citations · 58 across the 21 of their papers we have counts for

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5 papers · 1 filter

cs.SE2026

Generation of Web Apps with Agentic IDEs: An Empirical Assessment

Manuel Marceca, Maria Teresa Rossi, Leonardo Mariani

Agentic IDEs are among the most significant innovations in software engineering, aiming to accelerate application development through LLM-based agents that can assist developers du…

cs.SE2026

An Evaluation of Role-Based Multi-Agent Code Generation on Repository-Scale Problems

Benedetta Donato, Noah Hagar-Dent, Aaron Worsnop +2

Role-based multiagent code generation aims to make LLMs more effective on repository-scale problems, moving beyond small programming tasks. We evaluate this approach on 12 Java rep…

cs.PF2026★ 1 cited

GreenServ: Energy-Efficient Context-Aware Dynamic Routing for Multi-Model LLM Inference

Thomas Ziller, Shashikant Ilager, Alessandro Tundo +3

Large language models (LLMs) demonstrate remarkable capabilities, but their broad deployment is limited by significant computational resource demands, particularly energy consumpti…

cs.SE2026

"Where is My Troubleshooting Procedure?": Studying the Potential of RAG in Assisting Failure Resolution of Large Cyber-Physical System

Maria Teresa Rossi, Leonardo Mariani, Oliviero Riganelli +3

In today's complex industrial environments, operators must often navigate through extensive technical manuals to identify troubleshooting procedures that may help react to some obs…

cs.SE2026

Coverage-Guided Road Selection and Prioritization for Efficient Testing in Autonomous Driving Systems

Qurban Ali, Andrea Stocco, Leonardo Mariani +1

Autonomous Driving Assistance Systems (ADAS) rely on extensive testing to ensure safety and reliability, yet road scenario datasets often contain redundant cases that slow down the…