Tapajit Dey

Registered user since Sun 17 May 2020

Name:Tapajit Dey
Bio:

Tapajit Dey is a Post-Doctoral Researcher at Lero, the Science Foundation Ireland Research Centre for Software. He finished his PhD in Computer Science from the University of Tennessee with Dr. Audris Mockus. His research interests include Open Source and InnerSource development, empirical software engineering, mining software repositories, and data analytics. He received his Bachelors and Masters Degree from Indian Institute of Technology (IIT), Kharagpur and worked in IBM for 3 years before joining his PhD.

Country:Ireland
Affiliation:Lero - The Irish Software Research Centre and University of Limerick
Personal website:https://tapjdey.github.io
Research interests:Empirical Software Engineering, Data Science, Open Source Software

Contributions

MSR 2022 Committee Member in Program Committee within the Technical Papers-track
Publicity & Social Media Co-Chair in Organizing Committee
MSR 2021 Author of The Secret Life of Hackathon Code within the Technical Papers-track
Committee Member in Program Committee within the Technical Papers-track
Author of The Secret Life of Hackathon Code within the Hackathon-track
OSS 2021 Committee Member in Program Committee within the OSS 2021 Papers-track
ESEC/FSE 2020 Author of Do Code Review Measures Explain the Incidence of Post-Release Defects? Case Study Replications and Bayesian Networks within the Journal First-track
MSR 2020 Author of Detecting and Characterizing Bots that Commit Code within the Technical Papers-track
Author of A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits within the Data Showcase-track
ICSE 2021 Author of Representation of Developer Expertise in Open Source Software within the Technical Track-track
Author of Replication package for Representation of Developer Expertise in Open Source Software within the AE - Artifact Evaluation-track
ICSE 2019 Author of The Impact of Code Review Measures on Post-Release Defects: Replications and Bayesian Networks within the ROSE Festival-track