Daye Nam

Hello! I’m Daye Nam and I'm a Software Engineering Ph.D. student at the Institute for Software Research at Carnegie Mellon University. I am co-advised by Brad Myers, Bogdan Vasilescu, and Vencent Hellendoorn. I am also a member of the Natural Programming Group and STRUDEL.

I do research at the intersection of Software Engineering, Artificial Intelligence, and Human Computer Interaction, to improve API usability. Specifically, my research develops methods and tools for 1) API designers to detect and summarize API usability issues, and 2) programmers to learn and use APIs efficiently and correctly.

Prior to joining CMU, I completed my Master's degree in Computer Science from University of Southern California, where I worked in the Software Architecture Research Lab under the guidance of Nenad Medvidović. I received my Bachelor's degree at Yonsei University in Computer Science.


API Design Implications of Boilerplate Client Code

Daye Nam
ASE 2019 Student Competition, 2nd Place

MARBLE: Mining for Boilerplate Code to Identify API Usability Problems

Daye Nam, Amber Horvath, Andrew Macvean, Brad Myers, and Bogdan Vasilescu
ASE 2019

The Long Tail: Understanding the Discoverability of API Functionality

Amber Horvath, Sachin Grover, Sihan Dong, Emily Zhou, Finn Voichick, Mary Beth Kery, Shwetha Shinju, Daye Nam, Mariann Nagy, and Brad Myers
VL/HCC 2019

How Do Organizations Publish Semantic Markup? Three Case Studies Using Public Crawls

Daye Nam and Mayank Kejriwal
IEEE Computer, vol.51, no.6, pp.42-51.

EVA: A Tool for Visualizing Software Architectural Evolution

Daye Nam, Youn Kyu Lee, and Nenad Medvidovic
ICSE 2018 Tool Demonstration

Toward Predicting Architectural Significance of Implementation Issues

Arman Shahbazian, Daye Nam, Nenad Medvidovic
MSR 2018

Identifying Inter-Component Communication Vulnerabilities in Event-based Systems

Youn Kyu Lee, Daye Nam, and Nenad Medvidovic
USC Technical Report

SEALANT: A Detection and Visualization Tool for Inter-app Security Vulnerabilities in Android

Youn Kyu Lee, Peera Yoodee, Arman Shahbazian, Daye Nam, and Nenad Medvidovic
ASE 2017, Best Tool Paper Award