World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
7954
World Ranking
8342
National Ranking
73

Overview

Martin Pinzger is affiliated with the University of Klagenfurt in Austria. Their research primarily intersects with the field of Computer Science, focusing on various subfields including Information Systems, Artificial Intelligence, Software, Computer Networks and Communications, and Signal Processing.

The scientist's work spans across key topics such as:

  • Software Engineering Research
  • Software System Performance and Reliability
  • Software Reliability and Analysis Research
  • Advanced Malware Detection Techniques
  • Advanced Software Engineering Methodologies
  • Service-Oriented Architecture and Web Services
  • Software Testing and Debugging Techniques

Recent papers by Martin Pinzger showcase a diverse research agenda. Notable publications include:

  • "PentestGPT: An LLM-empowered Automatic Penetration Testing Tool" (2023), published in arXiv (Cornell University)
  • "Microservice API Evolution in Practice: A Study on Strategies and Challenges" (2024), published in Journal of Systems and Software
  • "The nature of build changes" (2021), published in Empirical Software Engineering
  • "A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows" (2021), published in Journal of Cybersecurity and Privacy
  • "What Kind of Questions Do Developers Ask on Stack Overflow? A Comparison of Automated Approaches to Classify Posts Into Question Categories" (2020), published by Gesellschaft für Informatik (GI)

Martin Pinzger has collaborated frequently with a set of co-authors, contributing to multiple joint studies. The most recurring collaborators include:

  • Stefan Raß
  • Christian Macho
  • Víctor Mayoral-Vilches
  • Johann Glock
  • Bernhard Dieber

The scientist's research appears regularly in a selection of publication venues. Prominent outlets for their work include:

  • arXiv (Cornell University)
  • Journal of Systems and Software
  • Zenodo (CERN European Organization for Nuclear Research)
  • Gesellschaft für Informatik (GI)
  • Technische Universität Berlin - Universitätsbibliothek

Best Publications

  • Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction

    B. Fluri;M. Wursch;M. Pinzger;H.C. Gall

  • Populating a Release History Database from version control and bug tracking systems

    M. Fischer;M. Pinzger;H. Gall

  • An exploratory study of the pull-based software development model

    Georgios Gousios;Martin Pinzger;Arie van Deursen

  • Can developer-module networks predict failures?

    Martin Pinzger;Nachiappan Nagappan;Brendan Murphy

  • Predicting the fix time of bugs

    Emanuel Giger;Martin Pinzger;Harald Gall

  • Visualizing multiple evolution metrics

    Martin Pinzger;Harald Gall;Michael Fischer;Michele Lanza

  • Analyzing and relating bug report data for feature tracking

    M. Fischer;M. Pinzger;H. Gall

  • Method-level bug prediction

    Emanuel Giger;Marco D'Ambros;Martin Pinzger;Harald C. Gall

  • Predicting defect densities in source code files with decision tree learners

    Patrick Knab;Martin Pinzger;Abraham Bernstein

  • Communication in open source software development mailing lists

    Anja Guzzi;Alberto Bacchelli;Michele Lanza;Martin Pinzger

  • Change Analysis with Evolizer and ChangeDistiller

    H.C. Gall;B. Fluri;M. Pinzger

  • Analysing Software Repositories to Understand Software Evolution

    Marco D'Ambros;Harald C. Gall;Michele Lanza;Martin Pinzger

  • Using source code metrics to predict change-prone Java interfaces

    Daniele Romano;Martin Pinzger

  • Relation of code clones and change couplings

    Reto Geiger;Beat Fluri;Harald C. Gall;Martin Pinzger

  • Comparing fine-grained source code changes and code churn for bug prediction

    Emanuel Giger;Martin Pinzger;Harald C. Gall

  • Detecting and visualizing inter-worksheet smells in spreadsheets

    Felienne Hermans;Martin Pinzger;Arie van Deursen

  • Detecting similar Java classes using tree algorithms

    Tobias Sager;Abraham Bernstein;Martin Pinzger;Christoph Kiefer

  • Supporting professional spreadsheet users by generating leveled dataflow diagrams

    Felienne Hermans;Martin Pinzger;Arie van Deursen

  • Domain-Specific Languages in Practice: A User Study on the Success Factors

    Felienne Hermans;Martin Pinzger;Arie Deursen

  • Detecting code smells in spreadsheet formulas

    Felienne Hermans;Martin Pinzger;Arie van Deursen

  • Predicting the fix time of bugs

    E. Giger;M. Pinzger;H.C. Gall

Frequent Co-Authors

Harald C. Gall
Harald C. Gall University of Zurich
Arie van Deursen
Arie van Deursen Delft University of Technology
Michele Lanza
Michele Lanza Universita della Svizzera Italiana
Shane McIntosh
Shane McIntosh McGill University
Giuliano Antoniol
Giuliano Antoniol Polytechnique Montréal
Andy Zaidman
Andy Zaidman Delft University of Technology
Massimiliano Di Penta
Massimiliano Di Penta University of Sannio
Abraham Bernstein
Abraham Bernstein University of Zurich
Ali Mesbah
Ali Mesbah University of British Columbia
Foutse Khomh
Foutse Khomh Polytechnique Montréal

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