Sung Kim

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45 SlideShares 872 Followers 207 Followings

Sung is an associate professor at the Hong Kong University of Science and Technology. He was a post-doc at the Program Analysis Group at MIT. He received his Ph.D. (thesis: Adaptive Bug Prediction By Analyzing Software History) in the Computer Science Department at the University of California, Santa Cruz. He has worked for Nara Vision Co. Ltd which is one of the leading Internet software companies in Korea for six years as a CTO. His research area is Software Engineering, focusing on software evolution, repository data mining, development social network mining, program analysis, and empirical studies. His chief research interest is programmer productivity, in particular, identifying faults

defect prediction deeplearning software testing fse hkust crash software engineering developers icse2011 deep api tensorflow machinelearning tensorboard software evolution debugging stackoverflow machine learning commit msr bug fault localization sna survey changes software development process tao intelligence code search engine instant code search tossing bug triage kenyon dsn2011

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Predicting Faults from Cached History

16 years ago • 1776 Views

Which Warnings Should I Fix First?

16 years ago • 1401 Views

Dissertation Defense

16 years ago • 16954 Views

Memories of Bug Fixes

16 years ago • 1448 Views

Signature Change Analysis

16 years ago • 1240 Views

Static and Adaptive Bug Fix Patterns

16 years ago • 783 Views

MeCC: Memory Comparison based Clone Detector

12 years ago • 1727 Views

Dealing with Noise in Defect Prediction

12 years ago • 1899 Views

"Crash Graphs: An Aggregated View of Multiple Crashes to Improve Crash Triage" by Sunghun Kim, Thomas Zimmermann and Nachiappan Nagappan.

12 years ago • 2032 Views

Micro Interaction Metrics for Defect Prediction (ESEC/FSE 2011)

11 years ago • 1702 Views

ReLink: Recovering Links between Bugs and Changes (ESEC/FSE 2011)

11 years ago • 1700 Views

OCAT: Object Capture based Automated Testing (ISSTA 2010)

11 years ago • 1438 Views

Kenyon: A Software Stratigraphy Platform (ESEC/FSE 2005)

11 years ago • 1758 Views

CosTriage: A Cost-Aware Algorithm for Bug Reporting Systems (AAAI 2011)

11 years ago • 2039 Views

ReCrash: Making crashes reproducible by preserving object states (ECOOP 2008)

11 years ago • 1716 Views

Self-defending software: Automatically patching errors in deployed software (SOSP 2009)

11 years ago • 1642 Views

BugTriage with Bug Tossing Graphs (ESEC/FSE 2009)

11 years ago • 2098 Views

Software Development Meets the Wisdom of Crowds

11 years ago • 1406 Views

Puzzle-Based Automatic Testing: Bringing Humans Into the Loop by Solving Puzzles (ASE 2012)

11 years ago • 1817 Views

Predicting Recurring Crash Stacks (ASE 2012)

11 years ago • 1614 Views

Defect, defect, defect: PROMISE 2012 Keynote

11 years ago • 4513 Views

How Do Software Engineers Understand Code Changes? FSE 2012

11 years ago • 1771 Views

A Survey on Automatic Test Generation and Crash Reproduction

10 years ago • 2054 Views

The Anatomy of Developer Social Networks

10 years ago • 835 Views

Automatic patch generation learned from human written patches

10 years ago • 9186 Views

Transfer defect learning

10 years ago • 3166 Views

STAR: Stack Trace based Automatic Crash Reproduction

10 years ago • 6957 Views

Personalized Defect Prediction

10 years ago • 3695 Views

MSR2014 opening

9 years ago • 17012 Views

Survey on Software Defect Prediction

9 years ago • 14133 Views

TinyBERT

Hoon Heo • 3 years ago

작고 빠른 딥러닝 그리고 Edge computing

StellaSeoYeonYang • 4 years ago

NLP Deep Learning with Tensorflow

seungwoo kim • 6 years ago

Tensorflow for Deep Learning(SK Planet)

Tae Young Lee • 6 years ago

[224]nsml 상상하는 모든 것이 이루어지는 클라우드 머신러닝 플랫폼

NAVER D2 • 6 years ago

Linear algebra

Sungbin Lim • 6 years ago

Generative adversarial networks

남주 김 • 6 years ago

CNN 초보자가 만드는 초보자 가이드 (VGG 약간 포함)

Lee Seungeun • 6 years ago

강화 학습 기초 Reinforcement Learning an introduction

Taehoon Kim • 7 years ago

Paper Reading : Enriching word vectors with subword information(2016)

정훈 서 • 6 years ago

One-Shot Learning

Jisung Kim • 6 years ago

지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016

Taehoon Kim • 7 years ago

Introduction to Amazon Web Services

Amazon Web Services • 12 years ago

부동산 텔레그램 봇

HoChul Shin • 7 years ago

Explanation on Tensorflow example -Deep mnist for expert

홍배 김 • 7 years ago

NDC 2016 김정주 - 기계학습을 활용한 게임어뷰징 검출

정주 김 • 7 years ago

Caffe framework tutorial2

Park Chunduck • 8 years ago

기계학습 / 딥러닝이란 무엇인가

Yongha Kim • 7 years ago

Google TensorFlow Tutorial

台灣資料科學年會 • 7 years ago

Synchronise your data between MySQL and MongoDB

Giuseppe Maxia • 10 years ago

[123] quality without qa

NAVER D2 • 8 years ago

깃헙으로 코드리뷰 하기

Ohgyun Ahn • 11 years ago

코드 리뷰 시스템 소개

Young-Ho Cha • 8 years ago

Introduce Docker

Yongbok Kim • 8 years ago

Slides for the day-long Lean Analytics workshop at the 2014 Lean Startup conference

Lean Analytics • 9 years ago

공짜 경제에서 어떻게 돈을 버는가?(How to Make Money in Free Economy)

Sangkyu Rho • 8 years ago

[1주차] 알파 유저를 위한 AWS 스터디

Amazon Web Services Korea • 8 years ago

AWS Summit Seoul 2015 - 모바일 및 IoT 환경을 위한 AWS 클라우드 플랫폼의 진화 (윤석찬, Markku Lepisto)

Amazon Web Services Korea • 8 years ago

Continous UI testing with Espresso and Jenkins

Sylwester Madej • 8 years ago

Evaluating the Usefulness of IR-Based Fault LocalizationTechniques

Alex Orso • 8 years ago

Sung is an associate professor at the Hong Kong University of Science and Technology. He was a post-doc at the Program Analysis Group at MIT. He received his Ph.D. (thesis: Adaptive Bug Prediction By Analyzing Software History) in the Computer Science Department at the University of California, Santa Cruz. He has worked for Nara Vision Co. Ltd which is one of the leading Internet software companies in Korea for six years as a CTO. His research area is Software Engineering, focusing on software evolution, repository data mining, development social network mining, program analysis, and empirical studies. His chief research interest is programmer productivity, in particular, identifying faults

defect prediction deeplearning software testing fse hkust crash software engineering developers icse2011 deep api tensorflow machinelearning tensorboard software evolution debugging stackoverflow machine learning commit msr bug fault localization sna survey changes software development process tao intelligence code search engine instant code search tossing bug triage kenyon dsn2011

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