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In software development, software architecture plays a vital role in developing and maintaining software systems. It is communicated through artifacts such as software architecture documentation (SAD) and software architecture models (SAM). However, maintaining consistency and traceability between these artifacts can be challenging. If there are inconsistencies or missing links, it can lead to errors, misunderstandings, and increased maintenance costs. This thesis proposes an approach for recovering traceability links of software architecture relations between natural language SAD and SAM. The approach involves the use of Pre-trained Language Models (PLMs) such as BERT and ChatGPT and supports different extraction modes and prompt engineering techniques for ChatGPT, as well as different model variants and training strategies for BERT. The proposed approach is integrated with ArDoCo, a tool that detects inconsistencies and recovers trace links between software artifacts. ArDoCo is used for pre-processing the SAD text and parsing the SAM, thus facilitating the traceability link recovery process. In order to assess the performance of the framework, a gold standard of SAD and SAM created from open-source projects is utilized. The evaluation shows that the ChatGPT approach has promising results in relation extraction with a recall of 0.81 and in traceability link recovery with an F1-score of 0.83, while BERT-based models struggle due to the lack of domain-specific training data.
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