One potential limitation of the contrastive captioning approach in BLens is its reliance on the quality and diversity of the training data. If the training set lacks sufficient examples of certain function types or naming conventions, the model may struggle to generalize effectively, particularly in the cross-project setting where distribution shifts occur. Additionally, the contrastive learning mechanism may not fully capture the nuanced relationships between different parts of binary code and their corresponding function names, especially in cases where the semantics are complex or context-dependent.
To improve this approach, one could incorporate more sophisticated techniques such as hierarchical attention mechanisms that can better model the relationships between different code components. Additionally, integrating external knowledge bases or ontologies related to programming languages and function semantics could enhance the model's understanding of context and improve its predictive capabilities. Furthermore, employing a more dynamic contrastive learning framework that adapts to the specific characteristics of the binary code being analyzed could lead to better alignment between code and function names.