Debugging as Evidence-Driven Reasoning: Visualization Opportunities in Data-Intensive Programming
For researchers and tool builders in visual debugging, this work provides a foundational characterization of debugging needs in data-intensive workflows, though it is an exploratory study with a small sample size.
The study characterizes debugging challenges in data-intensive programming through interviews with nine practitioners, identifying three core challenges (assembling fragmented evidence, detecting discrepancies, tracing state evolution) and translating them into visualization requirements (cross-artifact evidence alignment, expectation-grounded comparison, traceable state evolution) that current debuggers only partially address.
Visualization has been recognized as a valuable means of supporting debugging by externalizing runtime behavior that would otherwise remain hidden or scattered. However, most visual debugging research has focused on traditional software development settings, leaving the distinct challenges of data-intensive workflows largely uncharacterized. To build visual debugging support for these settings, we first need to characterize how practitioners debug in these settings and translate their challenges into concrete visualization opportunities. To this end, we conducted semi-structured interviews with nine participants from diverse data-intensive domains and analyzed the data using thematic analysis. Our analysis reveals three cross-cutting challenge: assembling fragmented evidence, detecting expected-observed discrepancies, and tracing state evolution across workflow components. We distill these challenges into three concrete requirements that current debuggers support only partially but that visualization is well suited to address: cross-artifact evidence alignment, expectation-grounded comparison, and traceable state evolution. Together, these requirements begin to characterize a design space for future visual debugging research in data-intensive programming.