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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…
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.…
Test Code Refactoring Unveiled: Where and How Does It Affect Test Code Quality and Effectiveness?
Luana Martins, Valeria Pontillo, Heitor Costa +3
Context. Refactoring has been widely investigated in the past in relation to production code quality, yet still little is known on how developers apply refactoring on test code. Sp…
Machine Learning-Based Test Smell Detection
Valeria Pontillo, Dario Amoroso d'Aragona, Fabiano Pecorelli +3
Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and…