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D-Index & Metrics

Computer Science

D-Index
34
Citations
4002
World Ranking
12291
National Ranking
164

Overview

Ferdian Thung is affiliated with Singapore Management University in Singapore and focuses primarily on the field of Computer Science. Their research spans several subfields, notably Information Systems, Artificial Intelligence, Signal Processing, Software, and Computer Networks and Communications.

The scientist's work covers diverse topics within software and data analysis domains. Main topics include:

  • Software Engineering Research
  • Topic Modeling
  • Advanced Malware Detection Techniques
  • Software Testing and Debugging Techniques
  • Software System Performance and Reliability
  • Software Engineering Techniques and Practices
  • Web Data Mining and Analysis

Ferdian Thung has published extensively across various venues. Notable frequent publication venues include:

  • arXiv (Cornell University)
  • ACM Transactions on Software Engineering and Methodology
  • IEEE Transactions on Software Engineering
  • 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
  • Empirical Software Engineering

Several recent papers reflect the focus and evolution of their research interests. These include:

  • "When and How to Make Breaking Changes," 2021, ACM Transactions on Software Engineering and Methodology
  • "BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems," 2021, IEEE Transactions on Software Engineering
  • "Duplicate Bug Report Detection: How Far Are We?," 2022, ACM Transactions on Software Engineering and Methodology
  • "AndroEvolve: automated Android API update with data flow analysis and variable denormalization," 2022, Empirical Software Engineering
  • "Representation Learning for Stack Overflow Posts: How Far Are We?," 2023, ACM Transactions on Software Engineering and Methodology

Frequent collaborators have contributed significantly to the body of work associated with Ferdian Thung. These co-authors include:

  • David Lo
  • Ting Zhang
  • Ivana Clairine Irsan
  • Lingxiao Jiang
  • Zhou Yang

Best Publications

  • Network Structure of Social Coding in GitHub

    F. Thung;T. F. Bissyande;D. Lo;Lingxiao Jiang

  • How to break an API: cost negotiation and community values in three software ecosystems

    Christopher Bogart;Christian Kästner;James Herbsleb;Ferdian Thung

  • Understanding the Test Automation Culture of App Developers

    Pavneet Singh Kochhar;Ferdian Thung;Nachiappan Nagappan;Thomas Zimmermann

  • Popularity, Interoperability, and Impact of Programming Languages in 100,000 Open Source Projects

    Tegawende F. Bissyande;Ferdian Thung;David Lo;Lingxiao Jiang

  • An Empirical Study of Bugs in Machine Learning Systems

    Ferdian Thung;Shaowei Wang;David Lo;Lingxiao Jiang

  • Automatic Defect Categorization

    Ferdian Thung;David Lo;Lingxiao Jiang

  • Automated library recommendation

    Ferdian Thung;David Lo;Julia Lawall

  • Automatic recommendation of API methods from feature requests

    Ferdian Thung;Shaowei Wang;David Lo;Julia Lawall

  • Extended comprehensive study of association measures for fault localization

    Lucia Lucia;David Lo;Lingxiao Jiang;Ferdian Thung

  • Categorizing the Content of GitHub README Files

    Gede Artha Azriadi Prana;Christoph Treude;Ferdian Thung;Thushari Atapattu

  • Overfitting in semantics-based automated program repair

    Xuan-Bach Dinh Le;Ferdian Thung;David Lo;Claire Le Goues

  • Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?

    Ting Zhang;Bowen Xu;Ferdian Thung;Stefanus Agus Haryono

  • BugsInPy: a database of existing bugs in Python programs to enable controlled testing and debugging studies

    Ratnadira Widyasari;Sheng Qin Sim;Camellia Lok;Haodi Qi

  • Code coverage and test suite effectiveness: Empirical study with real bugs in large systems

    Pavneet Singh Kochhar;Ferdian Thung;David Lo

  • Empirical Evaluation of Bug Linking

    T. F. Bissyande;F. Thung;Shaowei Wang;D. Lo

  • Theory and Practice, Do They Match? A Case with Spectrum-Based Fault Localization

    Tien-Duy B. Le;Ferdian Thung;David Lo

  • Deep Transfer Bug Localization

    Xuan Huo;Ferdian Thung;Ming Li;David Lo

  • To what extent could we detect field defects? an empirical study of false negatives in static bug finding tools

    Ferdian Thung;Lucia;David Lo;Lingxiao Jiang

  • When and How to Make Breaking Changes: Policies and Practices in 18 Open Source Software Ecosystems

    Chris Bogart;Christian Kästner;James Herbsleb;Ferdian Thung

  • Are faults localizable

    Lucia;Ferdian Thung;David Lo;Lingxiao Jiang

  • Should I follow this fault localization tool's output?

    Tien-Duy B. Le;David Lo;Ferdian Thung

  • Detecting similar applications with collaborative tagging

    Ferdian Thung;David Lo;Lingxiao Jiang

  • [Journal First] Overfitting in Semantics-Based Automated Program Repair

    Xuan-Bach D. Le;Ferdian Thung;David Lo;Claire Le Goues

Frequent Co-Authors

David Lo
David Lo Singapore Management University
Lingxiao Jiang
Lingxiao Jiang Singapore Management University
Julia Lawall
Julia Lawall French Institute for Research in Computer Science and Automation - INRIA
Gilles Muller
Gilles Muller French Institute for Research in Computer Science and Automation - INRIA
Claire Le Goues
Claire Le Goues Carnegie Mellon University
Tegawendé F. Bissyandé
Tegawendé F. Bissyandé University of Luxembourg
Christian Kästner
Christian Kästner Carnegie Mellon University
Christoph Treude
Christoph Treude Singapore Management University
Premkumar Devanbu
Premkumar Devanbu University of California, Davis
James D. Herbsleb
James D. Herbsleb Carnegie Mellon University

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