Debugging and Profiling (2020)
The lecture covers debugging techniques like logging and using debuggers, profiling tools for performance bottlenecks, static analysis for issue detection, and code linters for maintaining quality and consistency.
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In the lecture on debugging and profiling, various techniques for identifying and resolving issues in code are discussed. Debugging methods include using print statements and logging, which can provide insights into program behavior. Logging is preferred over print statements as it allows for filtering by severity levels and can log to various outputs. Debuggers, such as Python's pdb, enable developers to step through code execution, inspect variables, and set breakpoints. For more complex systems, third-party logs from web servers or databases may need to be examined. Profiling tools help identify performance bottlenecks in code, allowing developers to focus optimization efforts on the most resource-intensive areas. Techniques for timing code execution are also covered, emphasizing the importance of understanding real, user, and system time. Static analysis tools can catch potential issues without executing code, while code linters and formatters help maintain code quality and consistency. Overall, the lecture emphasizes the importance of both debugging and profiling in software development to ensure efficient and effective code.
- Debugging techniques include print statements, logging, and using debuggers.
- Profiling tools help identify performance bottlenecks in code.
- Static analysis tools can detect issues without running the code.
- Logging is preferred for its ability to filter and direct output.
- Code linters and formatters improve code quality and consistency.
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