6 citations · 8 across the 11 of their papers we have counts for
11 papers
How Do Developers Maintain and Evolve Their Agents' Instructions? An Empirical Study
Gianmario Voria, Alfonso Cannavale, Andrea De Lucia +3
Context. Autonomous coding agents are increasingly used in software development, shifting parts of the engineering process to AI assistance. While this automation brings clear bene…
Bias Ahead: Sensitive Prompts as Early Warnings for Fairness in Large Language Models
Gianmario Voria, Martina De Lucia, Alessandra Raia +3
Large Language Models (LLMs) are being increasingly integrated into software systems, offering powerful capabilities but also raising concerns about fairness. Existing fairness ben…
SCOPE: A Dataset of Stereotyped Prompts for Counterfactual Fairness Assessment of LLMs
Alessandra Parziale, Gianmario Voria, Valeria Pontillo +3
Large Language Models (LLMs) now serve as the foundation for a wide range of applications, from conversational assistants to decision support tools, making the issue of fairness in…
Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models
Gianmario Voria, Moses Openja, Foutse Khomh +2
The advent of transformer-based language models has reshaped how AI systems process and generate text. In software engineering (SE), these models now support diverse activities, ac…
Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation
Alessandra Parziale, Gianmario Voria, Valeria Pontillo +4
LLMs are increasingly used to boost productivity and support software engineering tasks. However, when applied to socially sensitive decisions such as team composition and task all…
Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework
Alessandra Parziale, Gianmario Voria, Valeria Pontillo +3
Nowadays, Large Language Models (LLMs) are foundational components of modern software systems. As their influence grows, concerns about fairness have become increasingly pressing.…
Contextual Fairness-Aware Practices in ML: A Cost-Effective Empirical Evaluation
Alessandra Parziale, Gianmario Voria, Giammaria Giordano +3
As machine learning (ML) systems become central to critical decision-making, concerns over fairness and potential biases have increased. To address this, the software engineering (…
Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?
Gianmario Voria, Rebecca Di Matteo, Giammaria Giordano +2
As machine learning (ML) systems are increasingly adopted across industries, addressing fairness and bias has become essential. While many solutions focus on ethical challenges in…
From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?
Gianmario Voria, Stefano Lambiase, Maria Concetta Schiavone +2
As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits,…
RECOVER: Toward Requirements Generation from Stakeholders' Conversations
Gianmario Voria, Francesco Casillo, Carmine Gravino +2
Stakeholders' conversations in requirements elicitation meetings hold valuable insights into system and client needs. However, manually extracting requirements is time-consuming, l…
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
Gianmario Voria, Giulia Sellitto, Carmine Ferrara +5
Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrim…