Updated: June 2026

Fabio Calefato — Curriculum Vitae

Contacts

Research Experience

Associate Professor, University of Bari, Dept. of Computer Science

Co-founder & CEO, PeoplewareAI s.r.l.

Tenure-track Assistant Professor, University of Bari, Dept. of Computer Science

Untenured Assistant Professor, University of Bari, Jonian Dept.

Postdoctoral Research Fellow, University of Bari, Dept. of Computer Science

Postdoctoral Research Fellow, University of Bari, Dept. of Computer Science

Postdoctoral Research Fellow, University of Bari, Dept. of Computer Science

Education

PhD in Computer Science

MSc in Computer Science

Inter-University Specialization School for Secondary Education in Physics, Computer Science, and Mathematics

Research Activity

My research primarily focuses on the intersection of Software Engineering and AI/ML, while also encompassing human factors in software development and globally distributed software engineering. Throughout my career, I have maintained a strong focus on empirical validation of research findings, conducting controlled experiments, case studies, and mining open-source software repositories. My work has consistently appeared in top-tier venues and has influenced both academic research and industry practices in software engineering. Below is a detailed description of some of my research activities, organized by recency and impact.

Generative AI for software engineering research and practices: In collaboration with leading international researchers, I am advancing four active workstreams that emerged from my participation in the 2023 and 2024 editions of the Copenhagen Symposium on Human-Centered Software Engineering AI, whose joint outcomes are summarized in The Copenhagen Manifesto (JSS 2024). The first workstream comprises an observational study mining self-admitted mentions of LLM usage in open-source projects. We examine how developers integrate AI assistants into their workflows across development tasks, content types, and usage purposes. Our study analyzed over 250,000 open-source repositories, identifying patterns in AI tool adoption and their impact on project metrics, with results published in IEEE TSE (2026). The second workstream aims to establish a comprehensive set of guidelines for software engineering research on LLMs. The initiative addresses the challenges of achieving reproducible results with LLMs by tackling their unique characteristics that affect study validity and reproducibility, providing researchers with concrete protocols for empirical evaluations (EMSE 2026, under review). The third workstream investigates the integration of AI in software engineering research methodologies. We examine how Generative AI tools can support various research tasks including qualitative analysis, systematic literature reviews, and human studies design, gathering perspectives from researchers about the changing landscape of empirical software engineering methods (TOSEM 2026). The fourth workstream investigates developer adoption of GenAI in practice, combining qualitative insights into sustained GenAI usage by software developers in Italian SMEs (TOSEM, under review) with biometric and perceptual evaluations of AI-assisted coding performance (EMSE, under review).

AI safety and regulatory compliance in healthcare systems: One of my current research interests focuses on developing frameworks and methodologies for ensuring continuous compliance and safety of AI systems in regulated healthcare environments. This research addresses the challenge of maintaining regulatory adherence while enabling continuous learning in medical AI applications. Working with medical professionals, we are developing an extended MLOps framework that integrates automated compliance verification, monitoring, and ethical oversight throughout the AI system lifecycle. We have systematically reviewed how AI is being integrated into healthcare systems to identify recurring barriers and enabling practices (Smart Health 2025), and explored generative approaches for medical data augmentation, evaluating the realism and clinical relevance of diffusion-model-based neuroimaging synthesis (J. Medical Systems 2025). The framework introduces systematic approaches for bias detection, fairness assessment, and performance monitoring across demographic groups, bridging the gap between responsible AI principles and clinical implementation requirements. This work has fostered collaborations with healthcare institutions and secured funding through national initiatives, including the DARE project for digital preventive healthcare solutions.

Human factors in software engineering: In my research I have extensively investigated how human factors such as personality traits, emotions, and social dynamics, influence software development processes, leveraging AI/ML techniques for analysis across various developer platforms and communication channels. In technical Q&A platforms like Stack Overflow, I have conducted comprehensive studies analyzing both technical aspects (such as community guidelines for effective questions) and social factors affecting answer success rates (MSR 2015, ESEM 2016, IST 2018, EMSE 2019). This work has led to the creation of gold standards for sentiment analysis (MSR 2018) and the development of ML-based methods to detect emotions and sentiment polarity in technical communication (IEEE Software 2020). Additionally, I have conducted cross-platform evaluations of sentiment analysis tools (MSR 2020) and performed extended replications to assess how the choice of sentiment analysis tools influences the validity of empirical studies (EMSE 2021). Beyond sentiment analysis, I have investigated how developer personalities influence collaboration in large software ecosystems like Apache (ICGSE 2018, IST 2019), with particular attention to how traits like agreeableness impact code review activities and pull request acceptance (ICGSE 2017). My work has shed light on the need for developing specialized tools for automatic personality detection from text in technical contexts (TOSEM 2021). With this line of research, I have demonstrated the critical importance of domain-specific approaches when analyzing developer communications, showing the limitations of general-purpose personality and sentiment analysis tools in software engineering contexts. Finally, I have also studied retention and disengagement factors of Open Source Software community participants, defining and validating a theoretical model of the activity rhythm of open-source project developers (SOHEAL 2019, EMSE 2022), and more recently examined how recognition mechanisms such as personal achievement badges shape developer engagement on GitHub (IST 2024).

Software engineering for AI-enabled systems: One of my research interests is focused on improving the development workflows of AI/ML-based systems through empirical studies and tool development. we have investigated the feasibility of AutoML for data-driven software engineering tasks (ESEM 2023) and subsequently conducted a comprehensive review of industry-leading AutoML tools to analyze their benefits and limitations in software engineering contexts (IST 2025). I have contributed to understanding MLOps practices by analyzing adoption patterns in open-source projects on GitHub (ESEM 2022). This work revealed key challenges in transitioning ML models from experimentation to production, leading to the development of an MLOps solution framework applied in healthcare contexts (CAIN 2022).

Software engineering for robotic systems: An emerging direction of my research investigates how generative AI and runtime-monitoring techniques can support the development and operation of robotic software built on ROS 2. We are exploring two complementary threads: the use of LLMs to synthesize and adapt state machines that govern runtime robot behavior, and the design of online anomaly-monitoring approaches that detect deviations in robot execution as they occur (RoSE 2026).

Industry-based research on the state of software engineering practices: I have participated in and continue to contribute to several industry-based global surveys to understand software engineering practices. The HELENA (Hybrid dEveLopmENt Approaches in software systems development) project has identified key characteristics of hybrid development approaches through analysis of 1,000+ developers across 50 countries since 2016. Our findings on agile process adoption patterns appeared in IEEE TSE (2021), significantly impacting our understanding of modern development methodologies. The NaPiRE project (Naming the Pain in Requirements Engineering) is a global survey initiative examining industrial practices and challenges in Requirements Engineering. Through biannual surveys, our large-scale academic collaboration develops a holistic theory of RE practices and problems, producing insights that guide problem-driven research. The Evolution of Post-Pandemic Work Policies project analyzes hybrid and remote work policies across companies worldwide through global surveys and academic collaboration. Our recent investigation of who actually governs the choice of work location maps the state-of-practice in post-pandemic remote work regulation (JSS 2026), providing evidence-based insights into emerging work patterns and helping organizations optimize their hybrid workplace policies.

Global software engineering: My research has addressed the challenges of software development distributed on a global scale. Key contributions include theoretical and empirical work on trust-building mechanisms and social awareness in virtual teams (CSCW 2013, CHASE 2012, IEEE Software 2013). I pioneered SocialCDE, a social awareness tool for fostering trust in distributed teams (ESEC/FSE 2013), which was awarded the 2011 Microsoft Software Engineering Innovation Award; this work demonstrated how social awareness tools can increase trust and improve coordination in global teams. I also made significant advances in communication barriers, developing and evaluating eConference, a real-time ML-based translation tool (ICGSE 2010-11, ESEM 2012, ESEM 2014, ESE 2016) that showed promising efficiency gains while identifying important trade-offs in distributed development activities; the tool was awarded the 2006 Eclipse Innovation Award by IBM. Additional contributions include an industrial action research study on communication tools in distributed agile teams (ICGSE 2020). My expertise in this domain is reflected in my service as General Chair for ICGSE 2019 and my role as Guest Editor for JSS special issue on Global Software Engineering (JSS 2021).

Awards

Bibliometrics

Selected Publications

Using Biometrics to Understand AI-Assisted Coding Performance and its Perception (2026 - under review)

Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape (2026 - under review)

From Early Adoption to Sustained Use: Understanding GenAI Usage Among Software Developers in Italian SMEs (2026 - under review)

Guidelines for Empirical Studies in Software Engineering involving Large Language Models (2026 - under review)

Self-Admitted GenAI Usage: A Mixed Methods Study of Software Projects on GitHub (2026)

Generative AI in Software Engineering Must Be Human-Centered: The Copenhagen Manifesto (2024)

Ph.D. Students Supervision

Dept. of Computer Science

Dept. of Computer Science, Ph.D. program cycle XXXV

Dept. of Computer Science, Ph.D. program cycle XXXII

Teaching

Reti di Calcolatori (Computer Networks) [6 ECTS]

Sicurezza nelle Reti e nei Sistemi Distribuiti (Computer Networks Security) [6 ECTS]

Social Computing [6 ECTS]

Software Solutions for Reproducible Experiments [2 ECTS]

Social Computing [5 ECTS]

Reti di Calcolatori (Computer Networks) [9 ECTS]

Reti di Calcolatori (Computer Networks) [6 ECTS]

Reti di Calcolatori (Computer Networks) [9 ECTS]

Mining Socio-technical Repositories [3 ECTS]

IT Tools Supporting Legal and Economic Research: Blockchain for Tracking Production and Transportation Chains [2 ECTS]

Classification Models in Software Engineering [2 ECTS]

Emotion Awareness in Social Computing [2 ECTS]

IT Tools Supporting Legal and Economic Research [2 ECTS]

Informatica (Computer Science) [9 ECTS]

Abilità Informatiche (Computer Skills) [4 ECTS]

Laboratorio di Informatica (C Programming Lab) [9 ECTS]

Linguaggi di Programmazione + Laboratorio (Programming Languages + Lab) [12 ECTS]

Laboratorio di Informatica (C Programming Lab) [5/6 ECTS]

Funded Research Projects

ARIANNA: ARtIficiAl iNtelligeNce for virtuAl meetings

DARE - Digital Lifelong Prevention

SERICS - SEcurity and RIghts In the CyberSpace / Spoke 9: SuReCare

Turning Padawans into Jedis: Using Worked Examples to Improve the Newcomer's Skills in Open Software Projects

C3 - Creative Cultural Collaboration

OpEn - Open up Entrepreneurship

VINCENTE - A Virtual Collective Intelligence Environment to Develop Sustainable Technology Entrepreneurship Ecosystems

PRONEM - Natural Language Processing for Global Software Development

INTERSOCIAL - Unleashing the Power of Social Networking for Enhancing Regional Systems

LOGIN - LOgistica INtegrata

Academic Service

Member of Department Executive Committee (Giunta del Dipartimento di Informatica)

Rector’s Delegate for the GARR Network

CS Dept. Director’s Delegate for Internship Programs

Associate Editor

Guest Editor

Review Board Member

Peer Reviews (partial list)

External Reviewer (Opponent) in Doctoral Defenses

Events Organization

Program Co-Chair

General Chair

Registered Reports Co-Chair

ERA Track Co-Chair

Steering Board Member

Track Co-Chair

Workshops & Tutorials Co-Chair

Workshops Co-Chair

Open-science Co-Chair

Publicity & Social Media Chair

Keynote Presentations

The Potential and Challenges of Personality Detection in Software Engineering Research

Facing Communication Challenges in Distributed Software Development

Membership in Program Committees

Membership in Doctoral Boards

Dept. of Computer Science

Dept. of Computer Science

Dept. of Computer Science

Dept. of Computer Science

Dept. of Computer Science

Dept. of Electrical and Information Engineering

Dept. of Electrical and Information Engineering

Research Visits

Invited Seminars

Software

RelAI™

xMLOps™

BehaViz™

References