2026 Tim Menzies: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	72	1686	1634	862	831	579	20013

Research.com Recognitions

2019 - IEEE Fellow For contributions to software engineering for artificial intelligence

Overview

Tim Menzies is affiliated with North Carolina State University in the United States. Their research output spans a wide range of topics within computer science, with a particular emphasis on software engineering and its associated subfields.

The main fields of study for Tim Menzies include:

Computer Science

Within this broader discipline, their work covers several subfields such as:

Information Systems
Software
Artificial Intelligence
Computer Networks and Communications
Signal Processing

The scientist's research topics focus on areas connected to software engineering and its reliability, including:

Software Engineering Research
Software Reliability and Analysis Research
Software Testing and Debugging Techniques
Software System Performance and Reliability
Advanced Malware Detection Techniques
Adversarial Robustness in Machine Learning
Ethics and Social Impacts of AI

Tim Menzies has published numerous papers in multiple venues. Frequent publication venues include:

arXiv (Cornell University)
IEEE Software
IEEE Transactions on Software Engineering
Empirical Software Engineering
ACM Transactions on Software Engineering and Methodology

Selected recent papers from their research include:

"Fairway: A Way to Build Fair ML Software," 2020, OPAL (Open@LaTrobe) (La Trobe University)
"Empirical Standards for Software Engineering Research," 2020, arXiv (Cornell University)
"Better Data Labelling With EMBLEM (and how that Impacts Defect Prediction)," 2020, IEEE Transactions on Software Engineering
"Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach," 2020, IEEE Transactions on Software Engineering
"FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes," 2022, IEEE Transactions on Software Engineering

The scientist frequently collaborates with other researchers. Coauthors with whom they have published most often include:

Rahul Yedida
Huy Tu
Joymallya Chakraborty
Rui Shu
Zhe Yu

Tim Menzies was recognized as an IEEE Fellow in 2019 for contributions related to software engineering for artificial intelligence.

Best Publications

Data Mining Static Code Attributes to Learn Defect Predictors

T. Menzies;J. Greenwald;A. Frank

1569
Citations
On the relative value of cross-company and within-company data for defect prediction

Burak Turhan;Tim Menzies;Ayşe B. Bener;Justin Di Stefano

823
Citations
Defect prediction from static code features: current results, limitations, new approaches

Tim Menzies;Zach Milton;Burak Turhan;Bojan Cukic

571
Citations
Heterogeneous Defect Prediction

Jaechang Nam;Wei Fu;Sunghun Kim;Tim Menzies

458
Citations
Automated severity assessment of software defect reports

T. Menzies;A. Marcus

419
Citations
The \{PROMISE\} Repository of Software Engineering Databases.

Jelber Sayyad Shirabad;Tim Menzies

387
Citations
Selecting Best Practices for Effort Estimation

T. Menzies;Z. Chen;J. Hihn;K. Lum

333
Citations
Problems with Precision: A Response to "Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors'"

T. Menzies;A. Dekhtyar;J. Distefano;J. Greenwald

324
Citations
On the Value of Ensemble Effort Estimation

E. Kocaguneli;T. Menzies;J. W. Keung

304
Citations
Tuning for software analytics

Wei Fu;Tim Menzies;Xipeng Shen

300
Citations
Bias in machine learning software: why? how? what to do?

Joymallya Chakraborty;Suvodeep Majumder;Tim Menzies

293
Citations
Local versus Global Lessons for Defect Prediction and Effort Estimation

T. Menzies;A. Butcher;D. Cok;A. Marcus

290
Citations
On the value of user preferences in search-based software engineering: a case study in software product lines

Abdel Salam Sayyad;Tim Menzies;Hany Ammar

251
Citations
Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

E. Kocaguneli;T. Menzies;A. Bener;J. W. Keung

246
Citations
Better cross company defect prediction

Fayola Peters;Tim Menzies;Andrian Marcus

243
Citations
On the use of relevance feedback in IR-based concept location

Sonia Haiduc;Andrian Marcus;Tim Menzies

240
Citations
What is wrong with topic modeling? And how to fix it using search-based software engineering

Amritanshu Agrawal;Wei Fu;Tim Menzies

238
Citations
Implications of ceiling effects in defect predictors

Tim Menzies;Burak Turhan;Ayse Bener

238
Citations
Automatic query reformulations for text retrieval in software engineering

Sonia Haiduc;Gabriele Bavota;Andrian Marcus;Rocco Oliveto

223
Citations
Knowledge maintenance: the state of the art

Tim Menzies

211
Citations
Local vs. global models for effort estimation and defect prediction

Tim Menzies;Andrew Butcher;Andrian Marcus;Thomas Zimmermann

202
Citations

Frequent Co-Authors

Bojan Cukic University of North Carolina at Charlotte

Burak Turhan Monash University

Leandro L. Minku University of Birmingham

Thomas Zimmermann Microsoft (United States)

Ayse Bener Toronto Metropolitan University

Jacky Keung City University of Hong Kong

Barry Boehm University of Southern California

Andrian Marcus The University of Texas at Dallas

Christian Bird Microsoft (United States)

Steve Easterbrook University of Toronto

External Links

Personal website of Tim Menzies Google Scholar page

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