Automated formal-specification-to-code trace links recovery using multi-dimensional similarity measures

Abstract

Formal specification techniques are widely used in safety-critical system development, where precise alignment between the specification components and their implementation counterparts is essential for conformance verification and program maintenance. Existing methods for establishing these trace links are often inefficient, requiring manual effort, and automated approaches based on textual similarity suffer from low precision. In this paper, we propose a novel automated method that incorporates multi-dimensional attributes of formal specification components to improve trace link recovery. The underlying principle supporting our method is that the names, structures, and relationships of specification components are typically preserved in their implementation. Our method contains four steps: (1) identifying the components in both the specification and the code, (2) extracting the multi-dimensional attributes for both specification components and code components, (3) calculating their similarities, and (4) predicting trace links through ranking and comparing to a threshold. We evaluate our method across three projects and demonstrate that it performs better in average precision, recall and F1-score than existing text-based similarity techniques, including Latent Semantic Indexing, Vector Space Model, Word2Vec embeddings, and LLM-based embeddings. These results confirm that our approach provides a more effective and reliable solution for automatically establishing trace links between formal specifications and code.