Related papers: Cluster Purge Loss: Structuring Transformer Embeddings for Equivalent Mutants Detection

Cluster Purge Loss: Structuring Transformer Embeddings for Equivalent Mutants Detection

URL: http://arxiv.org/abs/2507.20078v1
Date: Sat, 26 Jul 2025 23:07:11 GMT
Title: Cluster Purge Loss: Structuring Transformer Embeddings for Equivalent Mutants Detection
Authors: Adelaide Danilov, Aria Nourbakhsh, Christoph Schommer,
Abstract summary: We introduce a novel framework that integrates cross-entropy loss with a deep metric learning objective, termed Cluster Purge Loss.<n>We demonstrate state-of-the-art performance in the domain of equivalent mutant detection and produce a more interpretable embedding space.
Score: 0.05461938536945722
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recent pre-trained transformer models achieve superior performance in various code processing objectives. However, although effective at optimizing decision boundaries, common approaches for fine-tuning them for downstream classification tasks - distance-based methods or training an additional classification head - often fail to thoroughly structure the embedding space to reflect nuanced intra-class semantic relationships. Equivalent code mutant detection is one of these tasks, where the quality of the embedding space is crucial to the performance of the models. We introduce a novel framework that integrates cross-entropy loss with a deep metric learning objective, termed Cluster Purge Loss. This objective, unlike conventional approaches, concentrates on adjusting fine-grained differences within each class, encouraging the separation of instances based on semantical equivalency to the class center using dynamically adjusted borders. Employing UniXCoder as the base model, our approach demonstrates state-of-the-art performance in the domain of equivalent mutant detection and produces a more interpretable embedding space.

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