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Stefano Lambiase

0000-0002-9933-6203

Postdoctoral researcher (Software engineering; Empirical software engineering; Project management)

Italy

Stefano Lambiase is a postdoctoral researcher at the University of Salerno, Italy. His research focuses on Software Engineering, with a particular emphasis on Software Project Management, socio-technical aspects of software development, and Quantum Software Engineering. He has conducted both qualitative and quantitative empirical studies, employing methods such as software mining, Partial Least Squares Structural Equation Modeling (PLS-SEM), and Grounded Theory.

Publications

  • Good Fences Make Good Neighbours? on the Impact of Cultural and Geographical Dispersion on Community Smells
  • Just-in-time test smell detection and refactoring: The DARTS project
  • Quantumoonlight: A Low-Code Platform to Experiment with Quantum Machine Learning
  • Fright Not and Be Dispersed! Evaluating Cultural Dimensions Versus Software Communication and Collaboration Activities
  • Community Smell Detection and Refactoring in SLACK: The CADOCS Project
  • An empirical investigation into the influence of software communities’ cultural and geographical dispersion on productivity
  • Investigating Cultural Dispersion: on the Role of Cultural Differences in Software Development Teams
  • SERGE – Serious Game for the Education of Risk Management in Software Project Management
  • Dealing With Cultural Dispersion: a Novel Theoretical Framework for Software Engineering Research and Practice
  • SENEM: A software engineering-enabled educational metaverse
  • Cultural and Socio-Technical Aspects in Software Development
  • A Novel, Tool-Supported Catalog of Community Smell Symptoms
  • Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review
  • Uncovering Community Smells in Machine Learning-Enabled Systems: Causes, Effects, and Mitigation Strategies
  • Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering

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