31 citations · 71 across the 26 of their papers we have counts for
6 papers · 1 filter
Enhancing Automated Unit Test Generation for NLP Libraries Using Large Language Models
Amirhossein Deljouyi, Annibale Panichella, Andy Zaidman
Automated unit test generation tools like EvoSuite perform well on general-purpose software but often struggle with domain-specific software such as Natural Language Processing (NL…
Observability and Fault Injection for LLM-Based Multi-Agent Systems in Software Engineering
Zahra Seyedghorban, Egor Klimov, Arie van Deursen +2
Large Language Model-based multi-agent systems are increasingly explored for software engineering tasks, but they remain difficult to inspect, debug, and evaluate under controlled…
Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task
Enrique Barba Roque, Luís Cruz, Annibale Panichella
Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems such as clone detection, vulnera…
Test Case Selection for Deep Neural Networks: A Replication Study on LLMs for Code
Ali Asgari, Mitchell Olsthoorn, Annibale Panichella
Recently, test case selection (TCS) techniques have been explored to support the operational evaluation of deep neural networks (DNNs) under limited testing budgets, where labeling…
A Metamorphic Testing Approach to Diagnosing Memorization in LLM-Based Program Repair
Milan De Koning, Ali Asgari, Pouria Derakhshanfar +1
LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected by data leakage: large language…
Real-World Fault Detection for C-Extended Python Projects with Automated Unit Test Generation
Lucas Berg, Lukas Krodinger, Stephan Lukasczyk +4
Many popular Python libraries use C-extensions for performance-critical operations allowing users to combine the best of the two worlds: The simplicity and versatility of Python an…