Key class identification: a comprehensive dataset and a new GNN model

Abstract

Program comprehension is a critical task in software maintenance. As the scale of codebases expands, the required human effort increases exponentially. Key Class Identification (KCI) offers an effective solution to this challenge. Despite this, the absence of standardized benchmarks and the lack of robustness in most existing metric-based approaches across different software systems are major obstacles. In this paper, we first construct a comprehensive dataset to objectively evaluate KCI performance. Inspired by ensemble learning, we introduce a voting method to address key class labeling, representing the primary challenge in dataset construction. Additionally, we propose a novel GNN model that leverages graph transformer to capture information from directed class dependency networks for key class identification. Extensive experiments conducted on 170 software systems in our benchmark demonstrate that our approach achieves high accuracy of up to 93.1%, outperforming existing metric-based methods.