Exploration - Exploitation for Statistical Software Testing
Lecture slides:
- Exploration / Exploitation Inference for Statistical Software Testing
- Overview pt 1
- Software Testing
- Software Testing, Classification
- Correctness Testing
- Criteria
- Hybrid Statistical/Structural Approach
- Example
- Hybrid Statistical/Structural Approach 1
- Hybrid Statistical/Structural Approach, 2
- Uniform sampling of bounded program paths
- Hybrid Statistical/Structural Approach, 3
- Discussion
- Domain knowledge and search space
- 1st: Discriminant/Active learning
- 2nd: Generative learning
- Position of the problem
- Domain knowledge and search space 1
- Representation: Parikh map
- Distribution search space
- EXIST : Exploitation / Exploration Inference for Statistical Testing
- Selection Module
- Update Module
- Initialisation Module
- Seeded Initialization
- Initalisation Module 1
- Seeded Initalization 1
- Seeded Initialisation, 2
- Summary of EXIST
- Experimental Validation
- Experimental setting and goal
- Category I
- Category II
- Category III
- Problem art5
- Discussion
- Related Works
- Overview pt 2
- ML for Computer Science
- Evolution of Computing
- A case study (upcoming EGEE-Pascal Challenge)
- Goal: Grid modelling
- Modelling the grid: an ML problem
- Call to Arms
Author: Michele Sebag, University Of Paris Sud