2026 Nachiappan Nagappan: 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	63	2725	2644	1356	1306	215	18829

Nachiappan Nagappan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Nachiappan Nagappan sits on this spectrum.

32 publications 991+

This scientist: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Nachiappan Nagappan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Nachiappan Nagappan sits on this spectrum.

30 D-Index 131+

This scientist: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2020 - ACM Fellow For contributions to empirical software engineering and data-driven software development
2015 - ACM Distinguished Member

Overview

Nachiappan Nagappan is affiliated with Facebook (United States) in the United States. Their research primarily focuses on the field of Computer Science, with a strong emphasis on Software Engineering and its related subfields.

Their work covers multiple subfields of study including Information Systems, Software, Computer Networks and Communications, Artificial Intelligence, and Computer Science Applications. The main topics addressed in their research include Software Engineering Research, Software System Performance and Reliability, Software Engineering Techniques and Practices, Software Testing and Debugging Techniques, Open Source Software Innovations, Software Reliability and Analysis Research, and Advanced Malware Detection Techniques.

Among their recent publications are:

"A Tale of Two Cities: Software Developers Working from Home during the COVID-19 Pandemic" (2021) published in ACM Transactions on Software Engineering and Methodology
"Including Everyone, Everywhere: Understanding Opportunities and Challenges of Geographic Gender-Inclusion in OSS" (2021) published in IEEE Transactions on Software Engineering
"A Systematic Literature Review on Automated Software Vulnerability Detection Using Machine Learning" (2024) published in ACM Computing Surveys
"Large-scale intent analysis for identifying large-review-effort code changes" (2020) published in Information and Software Technology
"Feedback-driven semi-supervised synthesis of program transformations" (2020) published in Proceedings of the ACM on Programming Languages

Nachiappan Nagappan frequently publishes in venues such as arXiv (Cornell University), ACM Transactions on Software Engineering and Methodology, Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, OPAL (Open@LaTrobe) (La Trobe University), and Zenodo (CERN European Organization for Nuclear Research).

Their frequent co-authors include Peter C. Rigby, Chandra Maddila, Ayushi Rastogi, David Lo, and Gunnar Kudrjavets.

Awards received by Nachiappan Nagappan include:

ACM Fellow (2020) for contributions to empirical software engineering and data-driven software development
ACM Distinguished Member (2015)

Best Publications

Mining metrics to predict component failures

Nachiappan Nagappan;Thomas Ball;Andreas Zeller

1091
Citations
Use of relative code churn measures to predict system defect density

Nachiappan Nagappan;Thomas Ball

1037
Citations
Understanding network failures in data centers: measurement, analysis, and implications

Phillipa Gill;Navendu Jain;Nachiappan Nagappan

990
Citations
Software engineering for machine learning: a case study

Saleema Amershi;Andrew Begel;Christian Bird;Robert DeLine

980
Citations
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process

Thomas Zimmermann;Nachiappan Nagappan;Harald Gall;Emanuel Giger

883
Citations
Predicting defects using network analysis on dependency graphs

Thomas Zimmermann;Nachiappan Nagappan

726
Citations
Characterizing cloud computing hardware reliability

Kashi Venkatesh Vishwanath;Nachiappan Nagappan

687
Citations
Don't touch my code!: examining the effects of ownership on software quality

Christian Bird;Nachiappan Nagappan;Brendan Murphy;Harald Gall

464
Citations
Improving the CS1 experience with pair programming

Nachiappan Nagappan;Laurie Williams;Miriam Ferzli;Eric Wiebe

444
Citations
Static analysis tools as early indicators of pre-release defect density

Nachiappan Nagappan;Thomas Ball

399
Citations
On the value of static analysis for fault detection in software

J. Zheng;L. Williams;N. Nagappan;W. Snipes

383
Citations
The influence of organizational structure on software quality: an empirical case study

Nachiappan Nagappan;Brendan Murphy;Victor Basili

360
Citations
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows

Philip J. Guo;Thomas Zimmermann;Nachiappan Nagappan;Brendan Murphy

358
Citations
Usage and Perceptions of Agile Software Development in an Industrial Context: An Exploratory Study

A. Begel;N. Nagappan

352
Citations
Do Crosscutting Concerns Cause Defects

M. Eaddy;T. Zimmermann;K.D. Sherwood;V. Garg

345
Citations
Does distributed development affect software quality?: an empirical case study of Windows Vista

Christian Bird;Nachiappan Nagappan;Premkumar Devanbu;Harald Gall

332
Citations
Can developer-module networks predict failures?

Martin Pinzger;Nachiappan Nagappan;Brendan Murphy

326
Citations
A field study of refactoring challenges and benefits

Miryung Kim;Thomas Zimmermann;Nachiappan Nagappan

311
Citations
HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

Xin Xia;David Lo;Sinno Jialin Pan;Nachiappan Nagappan

304
Citations
Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study

N. Nagappan;T. Ball

272
Citations

Frequent Co-Authors

Thomas Zimmermann Microsoft (United States)

Laurie Williams North Carolina State University

Christian Bird Microsoft (United States)

Brendan Murphy Microsoft (United States)

Mladen A. Vouk North Carolina State University

Thomas Ball Microsoft (United States)

Harald C. Gall University of Zurich

Premkumar Devanbu University of California, Davis

Andreas Zeller Saarland University

David Lo Singapore Management University

External Links

Personal website of Nachiappan Nagappan Google Scholar page

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