Software Debugging

About this Course

In this course you will learn how to debug programs systematically using scientific methods and build several automated debugging tools in Python.

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At the end of this course you will have a solid understanding about systematic debugging, will know how to automate debugging and will have built several functional debugging tools in Python.

lesson 1
How Debuggers work
Theory: Scientific method and its application to debugging.
Fun fact: First bug in the history of computer science.
Practice: Building a simple tracer.
lesson 2
Asserting Expectations
Theory: Assertions in testing and in debugging.
Fun fact: The most expensive bug in history.
Practice: Improving the tracer.
lesson 3
Simplifying Failures
Theory: Strategy of simplifying failures. Binary search. Delta debugging principle.
Fun fact: Mozilla bugathon.
Practice: Building a delta debugger.
lesson 4
Tracking Origins
Theory: Cause-effect chain. Deduction. Dependencies. Slices.
Fun fact: Sherlock Holmes and Doctor Watson.
Practice: Improving the delta debugger.
lesson 5
Reproducing Failures
Theory: Types of bugs (Bohr bug, Heisenbug, Mandelbug, Schrodinbug). Systematic reproduction process.
Fun fact: Mad laptop bug.
Practice: Building a statistic debugging tool.
lesson 6
Learning from Mistakes
Theory: Bug database management. Classifying bugs. Bug maps. Learning from mistakes.
Fun fact: Programmer with the most buggy code.
Practice: Improving your tools and practicing on a real world bug database.