arXiv subjects

Filomena Ferrucci

Publications and source records attributed to Filomena Ferrucci.

At least 19 recordsLinked to original sources

arXiv2026

When AI Joins the Team! A Model of How AI Adoption Relates To Social Patterns in Software Engineering Teams

Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specialization work, AI is associated with higher knowledge-sharing peer interaction, which is in turn associated with fewer smells. In coordination work, AI is directly associated with higher communication quality, complementing rather than replacing human interaction. Contributions: We provide an empirically validated, TMS-grounded model showing that the AIcommunity-smell relationship is contingent on the type of collaboration, with a reusable instrument and evidence-based implications for research and practice.

cs.SE↗

arXiv2026

Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models

Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test, but its fixed formula may underuse diagnostic information contained in age, aspartate aminotransferase, alanine aminotransferase, and platelet count. We evaluated whether machine-learning-enhanced non-invasive testing (MLE-NIT) can improve advanced fibrosis detection while preserving this FIB-4 variable space. We used three biopsy-confirmed MASLD cohorts from China, Malaysia, and India (n=784). The Chinese cohort was split into 486 training and 54 internal validation/tuning patients; final performance was reported only on the Malaysian and Indian external cohorts. Models used five variables: age, FIB-4, aspartate aminotransferase, platelet count, and alanine aminotransferase. We compared FIB-4 with a shallow-deep neural network (s-DNN), TabPFN, and gpt-4o-2024-08-06. FIB-4 achieved external ROC-AUCs of 0.75 and 0.60 in Malaysia and India, respectively. TabPFN achieved 0.69 and 0.66, fine-tuned GPT-4o achieved 0.75 and 0.63, and the s-DNN achieved 0.77 and 0.67, respectively. The s-DNN contained only 354 trainable parameters, compared with 7,244,554 for TabPFN, yet provided a more balanced external operating profile. Calibration showed s-DNN Brier scores of 0.18 and 0.22, and permutation importance identified AST and FIB-4 as dominant variables. Compact non-linear MLE-NITs may enhance FIB-4-based fibrosis assessment without increasing clinical data requirements.

cs.LG↗

arXiv2026

Socio-Technical Well-Being of Quantum Software Communities: An Overview on Community Smells

Quantum computing has gained significant attention due to its potential to solve computational problems beyond the capabilities of classical computers. With major corporations and academic institutions investing in quantum hardware and software, there has been a rise in the development of quantum-enabled systems, particularly within open-source communities. However, despite the promising nature of quantum technologies, these communities face critical socio-technical challenges, including the emergence of socio-technical anti-patterns known as community smells. These anti-patterns, prevalent in open-source environments, have the potential to negatively impact both product quality and community health by introducing technical debt and amplifying architectural and code smells. Despite the importance of these socio-technical factors, there remains a scarcity of research investigating their influence within quantum open-source communities. This work aims to address this gap by providing a first step in analyzing the socio-technical well-being of quantum communities through a cross-sectional study. By understanding the socio-technical dynamics at play, it is expected that foundational knowledge can be established to mitigate the risks associated with community smells and ensure the long-term sustainability of open-source quantum initiatives.

cs.SE↗

arXiv2025

Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner's Perspective

Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open challenge. While previous research has explored high-level sustainability principles and policy recommendations, limited empirical evidence exists on how sustainability is practically managed in ML workflows. Existing studies predominantly focus on environmental sustainability, e.g., carbon footprint reduction, while missing the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings. To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner's perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.

cs.SE↗

arXiv2025

How Do Communities of ML-Enabled Systems Smell? A Cross-Sectional Study on the Prevalence of Community Smells

Effective software development relies on managing both collaboration and technology, but sociotechnical challenges can harm team dynamics and increase technical debt. Although teams working on ML enabled systems are interdisciplinary, research has largely focused on technical issues, leaving their socio-technical dynamics underexplored. This study aims to address this gap by examining the prevalence, evolution, and interrelations of community smells, in open-source ML projects. We conducted an empirical study on 188 repositories from the NICHE dataset using the CADOCS tool to identify and analyze community smells. Our analysis focused on their prevalence, interrelations, and temporal variations. We found that certain smells, such as Prima Donna Effects and Sharing Villainy, are more prevalent and fluctuate over time compared to others like Radio Silence or Organizational Skirmish. These insights might provide valuable support for ML project managers in addressing socio-technical issues and improving team coordination.

cs.SE↗

arXiv2025

Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering Purposes

Context: The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering (SE) processes. Research has explored their role within development teams, focusing on the specific purposes for which LLMs are used within SE tasks, such as artifact generation, decision-making support, and information retrieval. Despite the growing body of work on LLMs in SE, most studies have centered on broad adoption trends, neglecting the nuanced relationship between individual cognitive and behavioral factors and their impact on purpose-specific adoption. While factors such as perceived effort and performance expectancy have been explored at a general level, their influence on distinct SE purposes remains underexamined. This gap hinders the development of tailored LLM-based systems (e.g., Generative AI Agents) that align with engineers' specific needs and limits the ability of team leaders to devise effective strategies for fostering LLM adoption in targeted workflows. Objectives: For the reasons mentioned above, this study aims to study the individual factors that drive the choice to use LLMs for distinct SE purposes. Methods: To achieve the above-mentioned objective, we surveyed 188 software engineers to test the relationship between individual attributes related to technology adoption and LLM adoption across five key purposes, using structural equation modeling (SEM). The Unified Theory of Acceptance and Use of Technology (UTAUT2) was applied to characterize individual adoption behaviors. Results: The findings reveal that purpose-specific adoption is influenced by distinct factors, some of which negatively impact adoption when considered in isolation, underscoring the complexity of LLM integration in SE.

cs.SE↗

arXiv2025

LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison

The increasing volume of drug combinations in modern therapeutic regimens needs reliable methods for predicting drug-drug interactions (DDIs). While Large Language Models (LLMs) have revolutionized various domains, their potential in pharmaceutical research, particularly in DDI prediction, remains largely unexplored. This study thoroughly investigates LLMs' capabilities in predicting DDIs by uniquely processing molecular structures (SMILES), target organisms, and gene interaction data as raw text input from the latest DrugBank dataset. We evaluated 18 different LLMs, including proprietary models (GPT-4, Claude, Gemini) and open-source variants (from 1.5B to 72B parameters), first assessing their zero-shot capabilities in DDI prediction. We then fine-tuned selected models (GPT-4, Phi-3.5 2.7B, Qwen-2.5 3B, Gemma-2 9B, and Deepseek R1 distilled Qwen 1.5B) to optimize their performance. Our comprehensive evaluation framework included validation across 13 external DDI datasets, comparing against traditional approaches such as l2-regularized logistic regression. Fine-tuned LLMs demonstrated superior performance, with Phi-3.5 2.7B achieving a sensitivity of 0.978 in DDI prediction, with an accuracy of 0.919 on balanced datasets (50% positive, 50% negative cases). This result represents an improvement over both zero-shot predictions and state-of-the-art machine-learning methods used for DDI prediction. Our analysis reveals that LLMs can effectively capture complex molecular interaction patterns and cases where drug pairs target common genes, making them valuable tools for practical applications in pharmaceutical research and clinical settings.

cs.LG↗

arXiv2024

HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management

Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations when processing unstructured clinical data, including reliance on static databases and rule-based algorithms, frequently generating excessive alerts that lead to alert fatigue among healthcare providers. This paper introduces HELIOT, an innovative CDSS for adverse drug reaction management that processes free-text clinical information using Large Language Models (LLMs) integrated with a comprehensive pharmaceutical data repository. HELIOT leverages advanced natural language processing capabilities to interpret medical narratives, extract relevant drug reaction information from unstructured clinical notes, and learn from past patient-specific medication tolerances to reduce false alerts, enabling more nuanced and contextual adverse drug event warnings across primary care, specialist consultations, and hospital settings. An initial evaluation using a synthetic dataset of clinical narratives and expert-verified ground truth shows promising results. HELIOT achieves high accuracy in a controlled setting. In addition, by intelligently analyzing previous medication tolerance documented in clinical notes and distinguishing between cases requiring different alert types, HELIOT can potentially reduce interruptive alerts by over 50% compared to traditional CDSSs. While these preliminary findings are encouraging, real-world validation will be essential to confirm these benefits in clinical practice.

cs.AI↗

arXiv2024

Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review

Bots are software systems designed to support users by automating a specific process, task, or activity. When such systems implement a conversational component to interact with the users, they are also known as conversational agents. Bots, particularly in their conversation-oriented version and AI-powered, have seen their adoption increase over time for software development and engineering purposes. Despite their exciting potential, ulteriorly enhanced by the advent of Generative AI and Large Language Models, bots still need to be improved to develop and integrate into the development cycle since practitioners report that bots add additional challenges that may worsen rather than improve. In this work, we aim to provide a taxonomy for characterizing bots, as well as a series of challenges for their adoption for Software Engineering associated with potential mitigation strategies. To reach our objectives, we conducted a multivocal literature review, reviewing both research and practitioner's literature. Through such an approach, we hope to contribute to both researchers and practitioners by providing first, a series of future research routes to follow, second, a list of strategies to adopt for improving the use of bots for software engineering purposes, and third, enforce a technology and knowledge transfer from the research field to the practitioners one, that is one of the primary goal of multivocal literature reviews.

cs.SE↗

arXiv2024

Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process exemplifies the socio-technical nature of software development. Although LLMs influence the development process, software development remains fundamentally human-centric, necessitating an investigation of the human factors in this adoption. Thus, with this study we explore the factors influencing the adoption of LLMs in software development, focusing on the role of professionals' cultural values. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2) and Hofstede's cultural dimensions, we hypothesized that cultural values moderate the relationships within the UTAUT2 framework. Using Partial Least Squares-Structural Equation Modelling and data from 188 software engineers, we found that habit and performance expectancy are the primary drivers of LLM adoption, while cultural values do not significantly moderate this process. These findings suggest that, by highlighting how LLMs can boost performance and efficiency, organizations can encourage their use, no matter the cultural differences. Practical steps include offering training programs to demonstrate LLM benefits, creating a supportive environment for regular use, and continuously tracking and sharing performance improvements from using LLMs.

cs.SE↗

arXiv2024

A Catalog of Fairness-Aware Practices in Machine Learning Engineering

Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrimination against minorities. The software engineering community has responded by developing fairness-oriented metrics, empirical studies, and approaches. However, there remains a gap in understanding and categorizing practices for engineering fairness throughout the machine learning lifecycle. This paper presents a novel catalog of practices for addressing fairness in machine learning derived from a systematic mapping study. The study identifies and categorizes 28 practices from existing literature, mapping them onto different stages of the machine learning lifecycle. From this catalog, the authors extract actionable items and implications for both researchers and practitioners in software engineering. This work aims to provide a comprehensive resource for integrating fairness considerations into the development and deployment of machine learning systems, enhancing their reliability, accountability, and credibility.

cs.SE↗

arXiv2024

When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems

Context. The adoption of Machine Learning (ML)--enabled systems is steadily increasing. Nevertheless, there is a shortage of ML-specific quality assurance approaches, possibly because of the limited knowledge of how quality-related concerns emerge and evolve in ML-enabled systems. Objective. We aim to investigate the emergence and evolution of specific types of quality-related concerns known as ML-specific code smells, i.e., sub-optimal implementation solutions applied on ML pipelines that may significantly decrease both the quality and maintainability of ML-enabled systems. More specifically, we present a plan to study ML-specific code smells by empirically analyzing (i) their prevalence in real ML-enabled systems, (ii) how they are introduced and removed, and (iii) their survivability. Method. We will conduct an exploratory study, mining a large dataset of ML-enabled systems and analyzing over 400k commits about 337 projects. We will track and inspect the introduction and evolution of ML smells through CodeSmile, a novel ML smell detector that we will build to enable our investigation and to detect ML-specific code smells.

cs.SE↗

arXiv2024

An International and Multidisciplinary Teaching Experience with Real Industrial Team Project Development

This paper presents the design, objectives, experiences, and results of an international cooperation project funded by the European Commission in the context of the Erasmus Intensive Programme (IP, for short) designed to improve students' curricula. An IP is a short programme of study (minimum 2 weeks) that brings together university students and staff from at least three countries in order to encourage efficient and multinational teaching of specialist topics, which might otherwise not be taught at all. This project lasted for 6 years, covering two different editions, each one with three year duration. This project lasted for 6 years, covering two different editions, each one with three year duration. The first edition, named SAVRO (Simulation and Virtual Reality in Robotics for Industrial Assembly Processes) was held in the period 2008-2010, with the participation of three Universities, namely the Universitat Politecnica de Valencia (Spain), acting as IP coordinator, the Technische Universitat Kaiserslautern (Germany), and the Universita degli Studi di Salerno (Italy). The Universite de Reims Champagne-Ardenne (France) participated as a new partner in the subsequent edition (2011-2013) of the IP, renamed as HUMAIN (Human-Machine Interaction). Both editions of the teaching project were characterized by the same objectives and organizational aspects, aiming to provide educational initiatives based on active teaching through collaborative works between international institutions, involving industrial partners too. The aim of the paper is to illustrate the best practices that characterized the organization of our experience as well as to present some general recommendations and suggestions on how to devise computing academic curricula.

cs.CY↗

arXiv2023

Test Code Refactoring Unveiled: Where and How Does It Affect Test Code Quality and Effectiveness?

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. Specifically, there is still a lack of investigation into how developers typically refactor test code and its effects on test code quality and effectiveness. Objective. This paper presents a research agenda aimed to bridge this gap of knowledge by investigating (1) whether test refactoring actually targets test classes affected by quality and effectiveness concerns and (2) the extent to which refactoring contributes to the improvement of test code quality and effectiveness. Method. We plan to conduct an exploratory mining software repository study to collect test refactoring data of open-source Java projects from GitHub and statistically analyze them in combination with quality metrics, test smells, and code/mutation coverage indicators. Furthermore, we will measure how refactoring operations impact the quality and effectiveness of test code.

cs.SE↗

arXiv2022

Machine Learning-Based Test Smell Detection

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 effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of such detectors is still limited and dependent on thresholds to be tuned. Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells. Method: We plan to develop the largest dataset of manually-validated test smells. This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we plan to compare our approach with state-of-the-art heuristic-based techniques.

cs.SE↗

arXiv2022

On the Adoption and Effects of Source Code Reuse on Defect Proneness and Maintenance Effort

Context. Software reusability mechanisms, like inheritance and delegation in Object-Oriented programming, are widely recognized as key instruments of software design. These are used to reduce the risks of source code being affected by defects, other than to reduce the effort required to maintain and evolve source code. Previous work has traditionally employed source code reuse metrics for prediction purposes, e.g., in the context of defect prediction. Objective. However, our research identifies two noticeable limitations of current literature. First, still little is known on the extent to which developers actually employ code reuse mechanisms over time. Second, it is still unclear how these mechanisms may contribute to explain defect-proneness and maintenance effort during software evolution. We aim at bridging this gap of knowledge, as an improved understanding of these aspects might provide insights into the actual support provided by these mechanisms, e.g., by suggesting whether and how to use them for prediction purposes. Method. We propose an exploratory study aiming at (1) assessing how developers use inheritance and delegation during software evolution; and (2) statistically analyze the impact of inheritance and delegation on fault proneness and maintenance effort. The study will be conducted on the commits of 17 Java projects of the DEFECTS4J dataset.

cs.SE↗

arXiv2019

Not All Bugs Are the Same: Understanding, Characterizing, and Classifying the Root Cause of Bugs

Modern version control systems such as Git or SVN include bug tracking mechanisms, through which developers can highlight the presence of bugs through bug reports, i.e., textual descriptions reporting the problem and what are the steps that led to a failure. In past and recent years, the research community deeply investigated methods for easing bug triage, that is, the process of assigning the fixing of a reported bug to the most qualified developer. Nevertheless, only a few studies have reported on how to support developers in the process of understanding the type of a reported bug, which is the first and most time-consuming step to perform before assigning a bug-fix operation. In this paper, we target this problem in two ways: first, we analyze 1,280 bug reports of 119 popular projects belonging to three ecosystems such as Mozilla, Apache, and Eclipse, with the aim of building a taxonomy of the root causes of reported bugs; then, we devise and evaluate an automated classification model able to classify reported bugs according to the defined taxonomy. As a result, we found nine main common root causes of bugs over the considered systems. Moreover, our model achieves high F-Measure and AUC-ROC (64% and 74% on overall, respectively).

cs.SE↗

arXiv2019

Improving Change Prediction Models with Code Smell-Related Information

Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The negative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to comprehend source code, researchers empirically showed that the presence of code smells heavily impacts the change-proneness of the affected classes. On the basis of these findings, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, ie models having as goal that of indicating to developers which classes are more likely to change in the future, so that they may apply preventive maintenance actions. Specifically, we exploit the so-called intensity index - a previously defined metric that captures the severity of a code smell - and evaluate its contribution when added as additional feature in the context of three state of the art change prediction models based on product, process, and developer-based features. We also compare the performance achieved by the proposed model with the one of an alternative technique that considers the previously defined antipattern metrics, namely a set of indicators computed considering the history of code smells in files. Our results report that (i) the prediction performance of the intensity-including models is statistically better than that of the baselines and (ii) the intensity is a more powerful metric with respect to the alternative smell-related ones.

cs.SE↗