#!/usr/bin/env python
# coding: utf-8

# # Part IV: Semantic Fuzzing
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# This part introduces test generation techniques that take the _semantics_ of the input into account, notably the behavior of the program that processes the input.
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# * [Fuzzing with Constraints](FuzzingWithConstraints.ipynb) adds _semantic constraints_ to grammars. By solving these automatically, we can produce inputs that are syntactically _and_ semantically valid.
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# * [Grammar Mining](GrammarMiner.ipynb) shows how to extract an input grammar from a program by analyzing how individual parts of the input are processed.  The resulting grammars can be directly used for fuzzing.
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# * [Tracking Information Flow](InformationFlow.ipynb) shows how to track inputs throughout the program, in order to discover information leaks and further improve analysis techniques.
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# * [Concolic Fuzzing](ConcolicFuzzer.ipynb) analyzes program code to solve _path constraints_ in the program to cover branches and behaviors that are hard to reach.
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# * [Symbolic Fuzzing](SymbolicFuzzer.ipynb) works like concolic fuzzing, but does not require any executions at all.
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# * [Mining Function Specifications](DynamicInvariants.ipynb) extracts type information as well as pre- and postconditions from program executions – useful information for program analysis, testing, and verification.
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