Guiding Big Data Fuzz Testing with Boosted Coverage-Based Input Selection

Guiding Big Data Fuzz Testing with Boosted Coverage-Based Input Selection

van den Berg, Bo (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology)

Özkan, B. (mentor)
Decouchant, Jérémie (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-02

Big data applications are becoming increasingly popular. The importance of testing these applications increases with it. A recently proposed work called BigFuzz applies automated testing. The big data fuzzing tool shows very promising results. The aim of this research is to inspect how coverage guidance affects the performance of big data fuzzing. The current coverage usage is first described, then an extension is proposed, which is compared to the original. This work extends the BigFuzz tool with branch coverage guidance. The existing black-box fuzzer is substituted for a grey-box fuzzer, which is then extended to a boosted grey-box fuzzer. The two extensions both allow branch discovery. Boosted grey-box fuzzing shows to be the most efficient branch exploration mechanic. Furthermore, both extensions outperform the original tool regarding error detection.

Fuzz testing
Software testing
Test generation
Branch coverage
Big Data Analysis
DISC systems

http://resolver.tudelft.nl/uuid:6c93cf00-2be1-4e60-ab82-e2fc456d657f

Student theses

bachelor thesis

© 2021 Bo van den Berg