Architectural component models play a crucial role in achieving the desired software quality, as understandability of components and their interactions plays a key role in supporting the architectural understanding of a software system. In this article, we extend our previous studies on component models understandability. Our extensions study hierarchical understandability metrics, the impact of personal factors of participants like experience and expertise, and the combinations of both personal factors and the metrics (the previously studied and the newly introduced). The subjects of the study had to fully understand the functionalities of a number of components of an open source system by exploring the relationships of the components’ classes. Our results provide evidence that the hierarchical understandability metrics are significantly better in predicting the understandability effort than the models obtained in our previous studies or the models that include just the participants’ experiences. The participants’ experience plays an important role in the prediction but the obtained prediction models are not as accurate as the models that use the component level metrics.

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