# A Case Study on Automated Fuzz Target Generation for Large Codebases

## TL;DR

This paper presents an automated method for generating fuzz targets to reduce developer effort in applying fuzz testing to large codebases, leading to more efficient bug detection.

## Contribution

It introduces a novel automated fuzz target generation technique that simplifies fuzz testing integration for existing large codebases.

## Key findings

- Automated fuzz target generation increases bug detection.
- Reduces developer time for fuzz testing setup.
- Improves efficiency of fuzz testing in industry settings.

## Abstract

Fuzz Testing is a largely automated testing technique that provides random and unexpected input to a program in attempt to trigger failure conditions. Much of the research conducted thus far into Fuzz Testing has focused on developing improvements to available Fuzz Testing tools and frameworks in order to improve efficiency. In this paper however, we instead look at a way in which we can reduce the amount of developer time required to integrate Fuzz Testing to help maintain an existing codebase. We accomplish this with a new technique for automatically generating Fuzz Targets, the modified versions of programs on which Fuzz Testing tools operate. We evaluated three different Fuzz Testing solutions on the codebase of our industry partner and found a fully automated solution to result in significantly more bugs found with respect to the developer time required to implement said solution. Our research is an important step towards increasing the prevalence of Fuzz Testing by making it simpler to integrate a Fuzz Testing solution for maintaining an existing codebase.