Static source code analysis techniques are gaining relevance in automated
assessment of programming assignments as they can provide less rigorous
evaluation and more comprehensive and formative feedback. These techniques
focus on source code aspects rather than requiring effective code execution.
To this end, syntactic and semantic information encoded in textual data is
typically represented internally as graphs, after parsing and other
preprocessing stages. Static automated assessment techniques, therefore,
draw inferences from intermediate representations to determine the
correctness of a solution and derive feedback. Consequently, achieving the
most effective semantic graph representation of source code for the specific
task is critical, impacting both techniques? accuracy, outcome, and
execution time. This paper aims to provide a thorough comparison of the most
widespread semantic graph representations for the automated assessment of
programming assignments, including usage examples, facets, and costs for
each of these representations. A benchmark has been conducted to assess
their cost using the Abstract Syntax Tree (AST) as a baseline. The results
demonstrate that the Code Property Graph (CPG) is the most feature-rich
representation, but also the largest and most space-consuming (about 33%
more than AST).