Exploration - Exploitation for Statistical Software Testing

author: Michele Sebag, University of Paris-Sud 11

Categories

Top: Computer Science: Machine Learning: Computational Learning Theory

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*Slides*
0:00	Exploration / Exploitation Inference for Statistical Software Testing
1:42	Overview pt 1
2:43	Software Testing
4:26	Software Testing, Classification
5:54	Correctness Testing
8:52	Criteria
10:35	Hybrid Statistical/Structural Approach
11:46	Example
12:56	Hybrid Statistical/Structural Approach 1
13:28	Hybrid Statistical/Structural Approach, 2
15:15	Uniform sampling of bounded program paths
16:05	Hybrid Statistical/Structural Approach, 3
16:47	Discussion
17:59	Domain knowledge and search space
20:08	1st: Discriminant/Active learning
22:53	2nd: Generative learning
23:43	Position of the problem
25:46	Domain knowledge and search space 1
26:23	Representation: Parikh map
29:07	Distribution search space
30:03	EXIST : Exploitation / Exploration Inference for Statistical Testing
30:59	Selection Module
33:06	Update Module
34:30	Initialisation Module
36:28	Seeded Initialization
37:35	Initalisation Module 1
37:47	Seeded Initalization 1
38:07	Seeded Initialisation, 2
39:27	Summary of EXIST
40:06	Experimental Validation
41:31	Experimental setting and goal
42:04	Category I
43:08	Category II
43:39	Category III
44:25	Problem art5
44:54	Discussion
46:54	Related Works
48:32	Overview pt 2
48:56	ML for Computer Science
50:01	Evolution of Computing
50:51	A case study (upcoming EGEE-Pascal Challenge)
51:48	Goal: Grid modelling
53:35	Modelling the grid: an ML problem
55:09	Call to Arms

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