Linyi Li 李林翼 ly-li14@mails.tsinghua.edu.cn Senior Undergrad @ CS, Tsinghua

About

I am Linyi Li, a senior undergraduate @ Department of Computer Science and Technology, Tsinghua University.

My research interests are software engineering and machine learning, and their intersection.

Currently, I am working with Prof. Xiaoying Bai @ SEGroup, Tsinghua on Web API modeling, specification-based verification and scenario-based testing.

In Summer 2017, I did internship @ CMU on neural network explaining, fortunately advised by Prof. Matt Fredrikson.

I will join Prof. Tao Xie's ASE Group @ UIUC as a Ph.D student in Fall 2018.

[Curriculum Vitae]

News

Papers

  1. Kals Leino, Linyi Li, Shayak Sen, Anupam Datta, Matt Fredrikson
    Influence-Directed Explanations for Deep Convolutional Networks
    Arxiv Preprint 1802.03788
    [ArXiv]   [BibTex]
  2. Junyi Wang, Xiaoying Bai, Linyi Li, Haoran Ma, Zhicheng Ji
    A Model-Based Framework For Cloud API Testing
    Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual
    [PDF]   [BibTex]
  3. Junyi Wang, Xiaoying Bai, Haoran Ma, Linyi Li, Zhicheng Ji
    Cloud API Testing
    Verification and Validation Workshops (ICSTW), 2017 IEEE International Conference on Software Testing
    [PDF]   [BibTex]
  4. Linyi Li, Xiaoying Bai, Zhicheng Ji, Haoran Ma
    Lapis: Specification-Driven Web API Testing Based on Usage Scenario Model
    Submitted

Projects

Lapis

Lapis: Scenario-Based Automatic Web API Testing

Lapis is an automatic scenario-based Web API tester. The tool reads OpenAPI specification script and scenario definition, then generates and executes test cases automatically. Several evaluation experiments reveal its high efficiency and strength in Web API testing. PyLapis, the newest Python-based version supporting OpenAPI 3.0, is coming soon.

Neural Network Explanation

Application of Integrated Gradients on Diabetic Retinopathy Detection Network

The project applies integrated gradients, an influence analysis method, to a diabetic retinopathy detection convolutional neural network. The tool and framework supports multiple explaining configurations such as direct attributing and middle-layer filtered attributing. The attribution results can be used directly for lesion detection. The visualization result of each neuron's influence enabled further analysis of the neural network.

Poster
VEECloud

VEE@Cloud

A distributed web API testing system. Distributed cluster nodes send test request individually under center control. The distribution property makes it possible to generate heavy test load.

Paper
Ray Tracing

Ray Tracing Render Engine

  • A totally independent cross-platform graphics render engine.
  • Supported algorithms: Phong reflection model, ray tracing and photon mapping.
  • Supported light source: point light, and area light.
  • Supported model: DSL-specified model, and ".obj" format model.
  • Supported material: solid, and transparency with refraction.
Now preparing for open-sourcing...

Personal

Last Updated: May 9, 2018