Probabilistic Testing for Weak Memory Concurrency

The Probabilistic Concurrency Testing (PCT) algorithm provides theoretical guarantees for the probability of detecting concurrency bugs in a sequential consistency memory model, but its theoretical guarantees do not apply to weak memory concurrency. The weak memory concurrency refers to the modern compiler’s optimization that relaxes the...

DiscoTest: Evolutionary Distributed Concurrency Testing of Blockchain Consensus Algorithms

Distributed concurrency bugs (DC bugs) are bugs that are triggered by a specific order of events in distributed systems. Traditional model checkers systematically or randomly test interleavings but suffer from the state-space explosion in long executions. This thesis presents DiscoTest, a testing tool for DC bugs in blockchain consensus...

High-Level Mutations for JSON Typed Data in Big Data Fuzz Testing

Fuzzing in Big Data applications is a relatively new field which is still lacking effective tools to support automated testing. Recently, a framework called BigFuzz was published which made fuzz testing for big data systems feasible. But there was no solution to work with Big Data programs that use JSON typed data. Big Data systems often make...

Stacking High-Level Fuzz Mutations in Big Data Applications

The big data technology market size is expected to grow in the coming years. The advantages of having automated test tools for big data applications are becoming increasingly important. Fuzzing is an automated testing method which has been used in many different fields, but has not been frequently used in the big data domain as it poses several...

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

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...

Systematically Applying High-Level Mutations for Fuzz Testing Big Data Applications

As the amount of data worldwide continues to grow the big data field is becoming increasingly important. Fuzz testing has shown to be an effective testing tool, and recent work has applied fuzz testing to big data applications. This study aims to contribute to knowledge on fuzz testing big data applications by extending on BigFuzz, a state-of...

TabFuzz: High-level mutations for tabular data

Big Data is an expanding industry, yet exhaustive and automated testing of Big Data applications is still in its early stages. In the last few years, testing framework for Big Data applications have started appearing. BigFuzz is a program that uses fuzz testing for Big Data applications. Fuzz testing means generating random, potentially invalid...