9 citations · 14 across the 5 of their papers we have counts for
5 papers
Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
Yongjian Tang, Ezgi Sarikayak, Doruk Tuncel +2
Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Existing code summarization solut…
LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
Yongjian Tang, Thomas Runkler
Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper syste…
The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project
Robin Gröpler, Steffen Klepke, Jack Johns +12
Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quali…
The Few-shot Dilemma: Over-prompting Large Language Models
Yongjian Tang, Doruk Tuncel, Christian Koerner +1
Over-prompting, a phenomenon where excessive examples in prompts lead to diminished performance in Large Language Models (LLMs), challenges the conventional wisdom about in-context…
FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
Yongjian Tang, Rakebul Hasan, Thomas Runkler
Large Language Models (LLMs) have provided a new pathway for Named Entity Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods avoid the need for train…