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Computer Science

D-Index
37
Citations
4875
World Ranking
10900
National Ranking
686

Overview

Federica Sarro is affiliated with University College London in the United Kingdom. Their research domain is primarily within Computer Science, where they have contributed extensively to subfields like Information Systems, Software, Artificial Intelligence, Computer Networks and Communications, and Safety Research.

The scientist's research topics include Software Engineering Research, Software Reliability and Analysis Research, Software Testing and Debugging Techniques, Ethics and Social Impacts of AI, Adversarial Robustness in Machine Learning, Software Engineering Techniques and Practices, and Software System Performance and Reliability.

Federica Sarro has published numerous papers, with recent examples including:

  • "Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey," 2023, ACM Journal on Responsible Computing
  • "Empirical Standards for Software Engineering Research," 2020, arXiv (Cornell University)
  • "A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers," 2023, ACM Transactions on Software Engineering and Methodology
  • "A Survey of Performance Optimization for Mobile Applications," 2021, IEEE Transactions on Software Engineering
  • "Green AI," 2022, Proceedings of the 44th International Conference on Software Engineering

The frequent co-authors of Federica Sarro include:

  • Zhenpeng Chen (19 joint publications)
  • Mark Harman (18 joint publications)
  • Jie M. Zhang (15 joint publications)
  • Max Hort (14 joint publications)
  • Justyna Petke (12 joint publications)

Federica Sarro has published extensively in venues such as:

  • arXiv (Cornell University) - 33 publications
  • ACM Transactions on Software Engineering and Methodology - 10 publications
  • IEEE Transactions on Software Engineering - 8 publications
  • Empirical Software Engineering - 8 publications
  • Information and Software Technology - 4 publications

Best Publications

  • A Survey of App Store Analysis for Software Engineering

    William Martin;Federica Sarro;Yue Jia;Yuanyuan Zhang

  • The plastic surgery hypothesis

    Earl T. Barr;Yuriy Brun;Premkumar Devanbu;Mark Harman

  • Multi-objective software effort estimation

    Federica Sarro;Alessio Petrozziello;Mark Harman

  • Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey

    Unknown

  • The app sampling problem for app store mining

    William Martin;Mark Harman;Yue Jia;Federica Sarro

  • A Parallel Genetic Algorithm Based on Hadoop MapReduce for the Automatic Generation of JUnit Test Suites

    Linda Di Geronimo;Filomena Ferrucci;Alfonso Murolo;Federica Sarro

  • A genetic algorithm to configure support vector machines for predicting fault-prone components

    Sergio Di Martino;Filomena Ferrucci;Carmine Gravino;Federica Sarro

  • Clustering Mobile Apps Based on Mined Textual Features

    A. A. Al-Subaihin;F. Sarro;S. Black;L. Capra

  • Using tabu search to configure support vector regression for effort estimation

    A. Corazza;S. Di Martino;F. Ferrucci;C. Gravino

  • Exact Mean Absolute Error of Baseline Predictor, MARP0

    William B. Langdon;Javier Dolado;Federica Sarro;Mark Harman

  • Empirical Standards for Software Engineering Research

    Paul Ralph;Nauman bin Ali;Sebastian Baltes;Domenico Bianculli

  • Feature lifecycles as they spread, migrate, remain, and die in App Stores

    Federica Sarro;Afnan A. Al-Subaihin;Mark Harman;Yue Jia

  • Causal impact analysis for app releases in google play

    William Martin;Federica Sarro;Mark Harman

  • Investigating the relationship between price, rating, and popularity in the Blackberry World App Store

    Anthony Finkelstein;Mark Harman;Yue Jia;William Martin

  • Not going to take this anymore: multi-objective overtime planning for software engineering projects

    Filomena Ferrucci;Mark Harman;Jian Ren;Federica Sarro

  • Search-Based Software Project Management

    Filomena Ferrucci;Mark Harman;Federica Sarro

  • How effective is Tabu search to configure support vector regression for effort estimation

    A. Corazza;S. Di Martino;F. Ferrucci;C. Gravino

  • Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions

    Filomena Ferrucci;Carmine Gravino;Rocco Oliveto;Federica Sarro

  • Cross-validation based K nearest neighbor imputation for software quality datasets: An empirical study

    Jianglin Huang;Jacky Wai Keung;Federica Sarro;Yan-Fu Li

  • App Store Effects on Software Engineering Practices

    Afnan A. Al-Subaihin;Federica Sarro;Sue Black;Licia Capra

  • The importance of accounting for real-world labelling when predicting software vulnerabilities

    Matthieu Jimenez;Renaud Rwemalika;Mike Papadakis;Federica Sarro

Frequent Co-Authors

Filomena Ferrucci
Filomena Ferrucci University of Salerno
Mark Harman
Mark Harman University College London
Yue Jia
Yue Jia University College London
Emilia Mendes
Emilia Mendes Aarhus University
Rocco Oliveto
Rocco Oliveto University of Molise
Anthony Finkelstein
Anthony Finkelstein University College London
Licia Capra
Licia Capra University College London
Michael R. Lyu
Michael R. Lyu Chinese University of Hong Kong
Irwin King
Irwin King Chinese University of Hong Kong
Yves Le Traon
Yves Le Traon University of Luxembourg

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