Главная
Study mode:
on
1
Intro
2
Charm
3
Python is dynamic (3)
4
Downside: runtime errors
5
Solution: static analysis
6
How it works
7
Errors in the example
8
Layered code model
9
Parser
10
Standard or custom?
11
Resolving names
12
Resolving local names
13
PyFlakes tool
14
Dynamic challenge #1
15
Resolving imports
16
Dynamic challenge #4
17
The updated example
18
Resolving attributes
19
Primary type info sources
20
Static type systems for Python
21
Type inference in practice
22
Standard library annotations
23
Static-only type info
24
Dynamic challenge #7
25
More dynamic challenges
26
The results
27
When tools rock (2)
28
Wrap up
Description:
Explore the intricacies of static analysis for Python in this 51-minute conference talk from EuroPython 2013. Delve into the challenges posed by Python's dynamic nature and discover how static analysis can help prevent runtime errors. Learn about the layered code model, parsing techniques, and tools like PyFlakes. Examine the complexities of resolving names, imports, and attributes in Python code. Investigate static type systems, type inference, and the role of standard library annotations. Gain insights into overcoming various dynamic challenges and understand when static analysis tools truly excel in improving Python development.

Static Analysis of Python

EuroPython Conference
Add to list
0:00 / 0:00