Hi, This is Zihe Song.
I’m currently pursuing a Ph.D. degree in Computer Science at UTDallas, and working with Prof. Wei Yang. My interested research areas are software testing and reinforcement learning.

Education

• Ph.D. in computer Science, The University of Texas at Dallas, 2020 - Current
• M.S. in Computer Science, The University of Texas at Dallas, 2018 - 2020
• B.E. in Communication Engineering, University of Electronic Science and Technology of China, 2014 - 2018

Internship

• NetEase Fuxi Lab Summer Intern, 2020
  • Multi-style Imitation Learning Framework for Game Testing

Publication

• An Empirical Analysis of Compatibility Issues for Industrial Mobile Games
    Z. Song, Y. Chen, L. Ma, S. Lu, H. Lin, C. Fan, W. Yang
  Accepted by ISSRE 2022

• NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems
    S. Chen, C. Liu, M. Haque, Z. Song, W. Yang
  Accepted by ESEC/FSE 2022

• NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
    S. Chen, Z. Song, M. Haque, C. Liu, W. Yang
  Accepted by CVPR 2022

• An Empirical Analysis of UI-based Flaky Tests
    A. Romano, Z. Song, S. Grandhi, W. Yang, W. Wang
  Accepted by ICSE 2021

• An Automated Framework for Gaming Platform to Test Multiple Games
    Z. Song
  Accepted by ICSE 2020 SRC

Projects

• Availability Analysis of Existing Android Testing, Record and Replay Tools
  • Investigating the usability and effectiveness of existing record and replay tools in Android testing.
  • Analyzing the reproducibility of different types of bugs in Android apps.
• Automated Flaky Test Fixing Framework for Web UI Testing
  • Designing an automated fixing framework to detect and fix UI flakiness in web e2e testing.
  • Fixing flaky tests caused by asynchronization by recording and tracking DOM mutations.
• Data Consistency Level Testing and Validation in Multiple Databases
  • Designing algorithms to validate different data consistency levels (e.g., causal consistency).
  • Setting up on multiple databases (e.g., Galera Cluster, TiDB, FaunaDB) for evaluation.
• Evaluating the Performance of Neural Machine Translation Systems
  • Designing new adversarial attack methodology for existing Neural Machine Translation Systems.
  • Evaluating the efficiency-robustness of NMT systems under attack.

Skills

• Programming languages
  • C, Python, Java, SQL

Relevant Coursework

Machine Learning, Computer Vision, Statistics in Data Science, Database Design, Algorithms Analysis and Data Structures, Operating Systems, Linear Algebra, Calculus, Data Representation, Combinatorics and Graph Algorithms, Design and Analysis of Computer Algorithms