ConcernBERT is a BERT embedding model trained with triplet loss on class membership to encode concern-level semantics in Java entities, evaluated by recovering original classes from merged unlabeled groups on a new dataset of over 2M files, outperforming existing models.
A Semantic-Based Approach for Detecting and Decomposing God Classes
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abstract
Cohesion is a core design quality that has a great impact on posterior development and maintenance. By the nature of software, the cohesion of a system is diminished as the system evolves. God classes are code defects resulting from software evolution, having heterogeneous responsibilities highly coupled with other classes and often large in size, which makes it difficult to maintain the system. The existing work on identifying and decomposing God classes heavily relies on internal class information to identify God classes and responsibilities. However, in object-oriented systems, responsibilities should be analyzed with respect to not only internal class information, but also method interactions. In this paper, we present a novel approach for detecting God classes and decomposing their responsibilities based on the semantics of methods and method interactions. We evaluate the approach using JMeter v2.5.1 and the results are promising.
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cs.SE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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ConcernBERT: Learning Responsibilities Using Class Membership
ConcernBERT is a BERT embedding model trained with triplet loss on class membership to encode concern-level semantics in Java entities, evaluated by recovering original classes from merged unlabeled groups on a new dataset of over 2M files, outperforming existing models.