Gemma Catolino

We present MetaSERGE a metaverse-based implementation of the SERGE serious game for risk management education, built on top of SENEM, an educational metaverse engineered through software engineering practices. MetaSERGE transforms the original co-located, manually-facilitated board game into a collaborative digital environment by integrating avatar-based interaction, real-time multiplayer communication, automated game orchestration, and synchronized gameplay, while preserving the original educational workflow and reducing facilitator effort. We performed a preliminary exploratory evaluation involving six students, assessing usability and the application of risk management concepts using the same knowledge assessment adopted in the original SERGE study. Results indicate that participants perceived the system as usable and engaging, while successfully applying risk management concepts throughout gameplay.

Context. Fairness of machine learning (ML) systems is increasingly recognized as a critical non-functional requirement in software engineering. In modern ML-enabled systems, where data evolves continuously, and models are frequently retrained, current practices remain fundamentally reactive: fairness is assessed only after training, and violations caused by data drift are detected too late, triggering costly retraining cycles. Objective. We introduce FADE (Fairness Assessment under Dataset Evolution), a framework for preventive fairness stress testing of ML datasets. FADE assesses the structural robustness of a dataset by applying mutation-based perturbations to individual features and measuring their effect on data-level bias symptoms. Features whose fairness profile degrades under small changes are identified as fairness-fragile: latent vulnerabilities often residing in non-sensitive attributes that serve as proxies for discrimination. Method. We validate FADE through a two-phase empirical study on four benchmark datasets spanning socio-economic, criminal justice, healthcare, and financial domains. Results. FADE identified 30 high-risk configurations involving 21 unique fairness-fragile features, 76% of which are non-sensitive. In 93.3% of configurations, data-level fragilities translated into statistically significant fairness variations in downstream models, consistent across Random Forest and XGBoost. We also observe that perturbing a fragile feature can reduce rather than amplify model bias, revealing how deeply certain features are embedded in discriminatory behavior. Conclusion. FADE enables teams to surface fairness risks before model training, supporting a shift from reactive post-hoc assessment toward proactive data quality assurance in ML-enabled systems.

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, accelerating automation and decision-making. Yet, evidence shows that these models can reproduce or amplify social biases, raising fairness concerns. Recent work on neuron editing has shown that internal activations in pre-trained transformers can be traced and modified to alter model behavior. Building on the concept of knowledge neurons, neurons that encode factual information, we hypothesize the existence of biased neurons that capture stereotypical associations within pre-trained transformers. To test this hypothesis, we build a dataset of biased relations, i.e., triplets encoding stereotypes across nine bias types, and adapt neuron attribution strategies to trace and suppress biased neurons in BERT models. We then assess the impact of suppression on SE tasks. Our findings show that biased knowledge is localized within small neuron subsets, and suppressing them substantially reduces bias with minimal performance loss. This demonstrates that bias in transformers can be traced and mitigated at the neuron level, offering an interpretable approach to fairness in SE.

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 allocation, they raise concerns of fairness. Prior studies have revealed that LLMs may reproduce stereotypes; however, these analyses remain exploratory and examine sensitive attributes in isolation. This study investigates whether LLMs exhibit bias in team composition and task assignment by analyzing the combined effects of candidates' country and pronouns. Using three LLMs and 3,000 simulated decisions, we find systematic disparities: demographic attributes significantly shaped both selection likelihood and task allocation, even when accounting for expertise-related factors. Task distributions further reflected stereotypes, with technical and leadership roles unevenly assigned across groups. Our findings indicate that LLMs exacerbate demographic inequities in software engineering contexts, underscoring the need for fairness-aware assessment.