The rise of graph processing has led to an increase in the usage of graph databases and the availability of various frameworks. Graph databases have become more accessible and, in specific instances, can compete with relational databases. Testing an application with a relational database backend has shown limited test coverage, and current test generators cannot cover every branch condition in graph processing applications. There is a lack of test methods specifically designed for applications that utilize graph structures.
This paper presents PGFuzz, a coverage-guided, schema-aware fuzzer for graph processing applications. PGFuzz utilizes existing graph generators to generate inputs and applies graph-specific mutations to alter the graph state. These mutations are schema-aware, designed to cover the graph model search space and satisfy logical conditions from real-world applications. The mutations involve adding new graph elements, removing graph elements, modifying existing elements, altering property values, and violating graph constraints. When compared against existing graph generators and a random byte mutation approach on the nine real-world examples in our benchmark suite, PGFuzz demonstrates an increase in coverage over time and detects more logic errors than the other methods. PGFuzz can cover all previously uncovered branching.

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 challenges. BigFuzz, a new method which was proposed by a recent study, solves these problems and shows promising results. One of the BigFuzz contributions are high-level mutations, which are error type guided and schema aware mutations. This paper is answering the question: How does stacking high-level fuzz mutations affect the test performance for big data applications? It does so by creating different stacking strategies and evaluating the effect compared to the BigFuzz method. As evaluation metrics the research looks at the amount of unique failures per trial and the distribution of unique failures found. The three stacking strategies that have been developed for this project are: Random Stack, Smart Stack and Single Stack. This research has shown that there appear to be benefits to stacking high-level mutations. The results show that stacking algorithms find on average more unique failures in less trials than a non-stacking approach. Furthermore, is Smart Stack able to find unique failures more frequently. Empirical results suggest that stacking high-level mutations can provide an advantage over only mutating once.