What if your 100% code coverage score is meaningless? Learn how mutation testing and fuzzing find the critical bugs your unit tests miss.
#1about 3 minutes
Understanding the limitations of code coverage metrics
Different code coverage strategies like function, statement, and branch coverage can be misleading and turn testing into a numbers game.
#2about 4 minutes
Introducing mutation testing with a practical example
Mutation testing flips the testing paradigm by intentionally introducing small bugs (mutants) into your code to see if your existing tests can catch them.
#3about 3 minutes
Automating mutation testing in C# with Stryker
The Stryker framework for .NET automates the process of creating mutants, running tests against them, and generating an HTML report to identify weak spots in your test suite.
#4about 5 minutes
Exploring the different types of code mutations
Stryker can generate a wide variety of mutants by altering arithmetic operators, comparison logic, LINQ expressions, and even regular expressions to thoroughly challenge your tests.
#5about 4 minutes
Introducing the core concepts of fuzzing for quality
Fuzzing aims to find program crashes by feeding it mutated, semi-random input that is more effective than purely random data for discovering edge cases.
#6about 2 minutes
How coverage-guided fuzzing intelligently finds bugs
Coverage-guided fuzzing uses instrumentation to track which code paths are executed, prioritizing mutated inputs that explore new areas of the application.
#7about 3 minutes
Using AFL and SharpFuzz for fuzzing in C#
Combine the powerful AFL (American Fuzzy Lop) engine with the SharpFuzz library to create a test harness for fuzzing C# code, including whitelisting expected exceptions.
#8about 5 minutes
Analyzing fuzzing results and practical considerations
Learn to interpret the output from a fuzzing session to identify crash-causing inputs and consider the performance impact when integrating these tools into a CI/CD pipeline.
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