31 citations · 71 across the 26 of their papers we have counts for
6 papers · 1 filter
The Pursuit of Diversity: Multi-Objective Testing of Deep Reinforcement Learning Agents
Antony Bartlett, Cynthia Liem, Annibale Panichella
Testing deep reinforcement learning (DRL) agents in safety-critical domains requires discovering diverse failure scenarios. Existing tools such as INDAGO rely on single-objective o…
What Challenges Do Developers Face in AI Agent Systems? An Empirical Study on Stack Overflow & GitHub Issues
Ali Asgari, Annibale Panichella, Pouria Derakhshanfar +1
AI Agents have rapidly gained prominence in both research and industry as systems that extend large language models with planning, tool use, memory, and goal-directed action. Despi…
Metamorphic Testing of Deep Code Models: A Systematic Literature Review
Ali Asgari, Milan de Koning, Pouria Derakhshanfar +1
Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-relate…
Automated Test-Case Generation for REST APIs Using Model Inference Search Heuristic
Clinton Cao, Annibale Panichella, Sicco Verwer
The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-ar…
Test Wars: A Comparative Study of SBST, Symbolic Execution, and LLM-Based Approaches to Unit Test Generation
Azat Abdullin, Pouria Derakhshanfar, Annibale Panichella
Generating tests automatically is a key and ongoing area of focus in software engineering research. The emergence of Large Language Models (LLMs) has opened up new opportunities, g…
The Last Dependency Crusade: Solving Python Dependency Conflicts with LLMs
Antony Bartlett, Cynthia Liem, Annibale Panichella
Resolving Python dependency issues remains a tedious and error-prone process, forcing developers to manually trial compatible module versions and interpreter configurations. Existi…