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Software Debugging

Automating the Boring Tasks

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Nanodegree Program

Machine Learning Engineer

by kaggle

Make Predictive Models

Accelerate your career with the credential that fast-tracks you to job success.

About this Course

In this class you will learn how to debug programs systematically, how to automate the debugging process and build several automated debugging tools in Python.

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Software Debugging
Course Cost
Free
Timeline
Approx. 2 months
Skill Level
Intermediate
Included in Course

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Join the Path to Greatness

This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.

Free Course

Software Debugging

Enhance your skill set and boost your hirability through innovative, independent learning.

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Nanodegree Program

Machine Learning Engineer

by kaggle

Accelerate your career with the credential that fast-tracks you to job success.

Learn More
 
 

Course Leads

  • Andreas Zeller
    Andreas Zeller

    Instructor

  • Gundega Dekena
    Gundega Dekena

    Instructor

What You Will Learn

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 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 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 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 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 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.
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.

Prerequisites and Requirements

Basic knowledge of programming and Python at the level of Udacity CS101 or better is required. Basic understanding of Object-oriented programming is helpful.

See the Technology Requirements for using Udacity.

Why Take This Course

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.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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