Software comprehension in code-centric and model-driven settings: an experimental comparison of models and code

Abstract

Developers spend a large portion of their time on software comprehension, making it essential to understand how different artifacts – models and code – support this activity. While prior research has primarily explored the use of models as complementary aids for understanding code, limited evidence exists regarding their standalone effectiveness in conveying software’s structural and behavioral aspects. This gap is particularly relevant in contexts such as Model-Driven Engineering (MDE) and Low-Code/No-Code (LCNC) approaches, where models often serve as the primary or sole representation of software. The paper reports on two complementary controlled experiments that investigate how models and code contribute to understanding software structure and behavior. The first experiment compares comprehension when only reverse-engineered UML models or source code were available (code-centric settings), mimicking scenarios in agile or AI-assisted development where models are outdated or absent. The second experiment focuses on model-driven settings, examining whether providing generated code alongside platform-independent models improves comprehension and whether differences in code generators influence performance. The results demonstrate that models and code achieve comparable overall correctness, and thus models alone may be sufficient in model-centric scenarios where access to code is limited or unavailable. The experiments also reveal a consistent structure-behavior comprehension gap, show differences in comprehension across tasks regardless of artifact type, and indicate that adding code to models can both clarify and confuse comprehension depending on context.