Gianmario Voria

About

Exploring fairness, trust, and quality in the next generation of intelligent software systems.

I graduated magna cum laude in Computer Science from the University of Salerno, specializing in Software Engineering and IT Management. I am currently pursuing a PhD in Computer Science at the same university.

My research lies at the intersection of Software Engineering and Artificial Intelligence, with a strong focus on fairness, bias mitigation, trustworthy AI, and human-centered quality assurance.

Recently, I have been particularly interested in Large Language Models, autonomous coding agents, and how we can engineer responsible, transparent, and socially aware AI-enabled systems.

I work under the guidance of Prof. Fabio Palomba and Prof. Gemma Catolino.

Selected Publications

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ICSE2026

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Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework

MSR2026

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Exploring fairness, trust, and quality in the next generation of intelligent software systems.

Selected Publications

Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework

Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models

Publications

Journal Articles

RECOVER: Toward Requirements Generation from Stakeholders' Conversations

Fairness Set and Forgotten: Mining Fairness Toolkit Usage in Open-Source Machine Learning Projects

Fair and square? Evaluating fairness of LLM-generated synthetic datasets

Fairness on a Budget, Across the Board: A Cost-Effective Evaluation of Fairness-Aware Practices Across Contexts, Tasks, and Sensitive Attributes

Examining the Impact of Bias Mitigation Algorithms on the Sustainability of ML-enabled Systems: A Benchmark Study

Fairness-aware practices from developers’ perspective: A survey

Conference & Workshop Papers

Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework

Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models

Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation

Toward Measuring Prompt Quality: A Preliminary Investigation on Prompt Smells

SCOPE: A Dataset of Stereotyped Prompts for Counterfactual Fairness Assessment of Large Language Models

Bias Ahead: Sensitive Prompts as Early Warnings for Fairness in Large Language Models

A catalog of fairness-aware practices in machine learning engineering

Contextual Fairness-Aware Practices in ML: A Cost-Effective Empirical Evaluation

From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?

FRINGE: context-aware FaiRness engineerING in complex software systEms

Is Attention All You Need? Toward a Conceptual Model for Social Awareness in Large Language Models

Community Smell Detection and Refactoring in SLACK: The CADOCS Project

Experience

Journal Reviews

Program Committee Member

Organizing Committee and Chair Roles

Student Volunteer