Fabio Palomba

Assistant Professor (Tenured)

University of Salerno

Italy

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About

Fabio Palomba is an Assistant Professor (Tenured) at the University of Salerno, Italy. His research areas include machine learning project classification, mining techniques for software vulnerability, and the lifecycle of ML-specific code smells. He has published extensively on topics related to non-functional requirements in ML-enabled systems and the challenges of anomaly detection in software engineering.

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Collaborators (16)

Xiaozhou Li

Free University of Bozen-Bolzano

ITALY

Fabio Palomba

Research Interests

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About

Articles (29)

Back to the Roots: Assessing Mining Techniques for Java Vulnerability-Contributing Commits

Uncovering Community Smells in Machine Learning-Enabled Systems: Causes, Effects, and Mitigation Strategies

Fairness-aware practices from developers’ perspective: A survey

RECOVER: Toward Requirements Generation From Stakeholders’ Conversations

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

Into the ML-Universe: An improved classification and characterization of machine-learning projects

When code smells meet ML: on the lifecycle of ML-specific code smells in ML-enabled systems

Classification and challenges of non-functional requirements in ML-enabled systems: A systematic literature review

LLM-Based Automation of COSMIC Functional Size Measurement From Use Cases

An empirical investigation into the capabilities of anomaly detection approaches for test smell detection

On the adoption and effects of source code reuse on defect proneness and maintenance effort

The quantum frontier of software engineering: A systematic mapping study

Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can It Be?

Test Code Flakiness in Mobile Apps: The Developer’s Perspective

Machine learning-based test smell detection

An Empirical Investigation Into the Influence of Software Communities’ Cultural and Geographical Dispersion on Productivity

Fairness-aware machine learning engineering: how far are we?

A Large-Scale Empirical Investigation into Cross-Project Flaky Test Prediction

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

The anatomy of a vulnerability database: A systematic mapping study

“Through the looking-glass …” An empirical study on blob infrastructure blueprints in the Topology and Orchestration Specification for Cloud Applications

A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms

The making of accessible Android applications: an empirical study on the state of the practice

On the use of artificial intelligence to deal with privacy in IoT systems: A systematic literature review

The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study

Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics

Evolving software forges: An experience report from Apache Allura

Lightweight Assessment of Test-Case Effectiveness Using Source-Code-Quality Indicators

Success and Failure in Software Engineering: A Followup Systematic Literature Review

Collaborators (16)

Xiaozhou Li

Sergio Moreschini

Stefano Dalla Palma

Giovanni Grano

Gemma Catolino

Giuliana Vitiello

Stefano Lambiase

Harald Gall

Fahed Alkhabbas

Andrea De Lucia

Davide Taibi

Zheying Zhang

Dario Di Nucci

Damian Andrew Tamburri

Carmine Gravino

Filomena FERRUCCI

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