Automated Testing for Data-Intensive Scalable Computing

Abstract

Data-Intensive Scalable Computing (DISC) systems such as Apache Spark, Hadoop, Flink, and Beam have become central to modern data processing. These systems enable developers to write applications as dataflows composed of user-defined functions operating over large and often unstructured datasets. However, testing such applications and the underlying frameworks remains a significant challenge. Conventional testing techniques struggle in this space due to the complexity of dataflow semantics, the scale and heterogeneity of input data, and the need to reason about operator interactions, schema variability, and program logic simultaneously. This thesis presents a set of techniques for extracting and leveraging rich, fine-grained properties that are unique to DISC workloads in order to inform automated input generation for effective testing across various levels of the data-centric software stack. The first thrust of this work focuses on generating inputs that can avoid trivial parsing errors and effectively exercise the deeper logic in DISC applications. By analyzing how code interacts with different parts of the input data, we test the code where it matters most instead of wasteful fuzzing cycles finding parsing issues. The second thrust explores how to create realistic and meaningful test data that reflects the structure and semantics of real-world inputs, while still achieving high coverage and fault detection. The final part of this work shifts focus to DISC frameworks, recognizing that applications are only as reliable as the systems they run on. By generating diverse dataflow programs, we systematically test internal components like optimizers, extending fuzzing to the entire DISC stack.