Andrea Lops

I am a researcher in Computer Science at the Polytechnic University of Bari, specializing in automated software testing and Large Language Models (LLMs). My academic journey is rooted in a strong foundation in software engineering, artificial intelligence, and automation, with a particular emphasis on developing methodologies that bridge the gap between theoretical advancements in AI and the practical needs of software development. Currently pursuing a Ph.D. in Electrical and Information Engineering, my work is centered on enhancing the scalability and reliability of AI-driven software testing systems.

My research primarily focuses on automated test generation using LLMs, exploring methods to create and evaluate unit and class-level test cases efficiently. A significant part of my work has been dedicated to improving test quality assessment through quantitative and qualitative evaluation frameworks, ensuring that AI-generated tests maintain high coverage and reliability. I have contributed to the field by developing AgoneTest, an advanced system that automates the entire software testing lifecycle, from test generation to assessment, leveraging the capabilities of state-of-the-art LLMs.​

Throughout my career, I have been involved in various research and development projects spanning machine learning, natural language processing, and software automation. My experience extends beyond academia, as I have worked as a Machine Learning Engineer at Wideverse Srl, an innovative startup focusing on AI, virtual reality, and augmented reality applications. My role involved designing and implementing AI-driven solutions, including conversational agents using Rasa and Mozilla DeepSpeech, as well as developing mobile and web applications for public administration and enterprise environments.

My academic background includes a Master’s degree in Computer Engineering (Information Systems) with the highest honors, during which I researched IoT architectures for monitoring plant diseases, developed bidirectional transpilers for mobile applications, and designed machine learning models for malware analysis using PySpark. I also hold a Bachelor’s degree in Computer Engineering and Automation, where I explored cloud-based architectures for IoT-based biometric data collection.

Fluent in multiple programming languages, including Java, Python, Kotlin, Swift, C, JavaScript, and TypeScript, I have hands-on experience in software development and AI research. My technical expertise extends to cloud computing (Microsoft Azure Certified), data analysis, and software optimization. I am deeply committed to advancing the field of automated software testing and leveraging AI to enhance efficiency, accuracy, and accessibility in modern software development.