Jun-Wei Lin, Ph.D. in Software Engineering, UC Irvine

I am a software engineer, researcher, best-selling author, and top-rated instructor. Currently, I work at MGM Resorts International and support the Site Reliability Engineering team. Prior to MGM, I received my Ph.D. in Software Engineering at the School of ICS, University of California, Irvine, advised by Prof. Sam Malek. My research interest is software analysis and testing with natural language processing and machine learning techniques (AI for SE).

Cloud-Native Development and CI/CD: Microsoft Azure and DevOps, Jenkins, Robot Framework, Selenium
Web App Development: Django, .NET Core, jQuery, Bootstrap
Programming Languages: Python, C#, Java, PowerShell, JavaScript, SQL
Machine Learning and Natural Language Processing: scikit-learn, gensim, NLTK
Microsoft Certified Azure Developer Associate and AWS Certified Cloud Practitioner

ROUTE: Roads Not Taken in UI Testing
Jun-Wei Lin, Navid Salehnamadi, and Sam Malek
ACM Transactions on Software Engineering and Methodology. (accepted to appear)
GUI Test Transfer from Web to Android
Jun-Wei Lin and Sam Malek
15th IEEE International Conference on Software Testing, Verification and Validation (ICST 2022). (26% acceptance rate) [pdf]
Test Automation in Open-Source Android Apps: A Large-Scale Empirical Study
Jun-Wei Lin, Navid Salehnamadi, and Sam Malek
35th International Conference on Automated Software Engineering (ASE 2020). (23% acceptance rate) [pdf] [slides]
Test Transfer Across Mobile Apps Through Semantic Mapping
Jun-Wei Lin, Reyhaneh Jabbarvand, and Sam Malek
34th International Conference on Automated Software Engineering (ASE 2019). (21% acceptance rate) [pdf] [slides]
Web Scraping and Data Analysis with Python (in Chinese)
Jun-Wei Lin and Hubert Lin
DrMaster Press, 2018. ISBN: 9789864343386
Nemo: Multi-Criteria Test-Suite Minimization with Integer Nonlinear Programming
Jun-Wei Lin, Reyhaneh Jabbarvand, Joshua Garcia, and Sam Malek
40th International Conference of Software Engineering (ICSE 2018). (21% acceptance rate) [pdf] [slides]
Using Semantic Similarity in Crawling-Based Web Application Testing
Jun-Wei Lin, Farn Wang, and Paul Chu
IEEE International Conference on Software Testing, Verifaction and Validation (ICST 2017). (27% acceptance rate) [pdf] [slides]

Kaggle Competition: Rainfall Prediction (7/126, top 6%)
Used ensembles (e.g., Random Forest and XGBoost) and feature engineering (e.g., missing data handling) to predict rainfall on 40K data points of infrared information
PTT Web Crawler (400+ Stars and 210+ forks on GitHub)
A Python command-line tool to crawl and parse data from PTT, the largest local online community in Taiwan
Bulletin Board for Government Job Opportunities (800+ daily active users)
A Django website hosted on AWS, parsing and visualizing open data from Taiwan's government
Predicting Best Answers for Questions on Stack Overflow
Applied various ML models (e.g., Random Forest and XGBoost) and NLP techniques (e.g., Latent Semantic Indexing) to predict best answers for 44K questions on Stack Overflow. Outperformed baseline by 8.5%