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Although unsupervised Information Retrieval (IR) and Machine Learning (ML) techniques are widely adopted for automated trace link recovery, their effectiveness is often limited by the quality and structure of the underlying artifacts. In practice, these approaches assume that meaningful traceability signals are embedded in textual data, an assumption that rarely holds in industrial settings with sparse, inconsistent, or unbalanced documentation. Furthermore, conventional evaluation metrics (e.g., precision, recall, F1) can misrepresent performance when data characteristics are not explicitly considered. We introduce TraceXplainer, an information-theoretic framework for evaluating the reliability and limits of unsupervised traceability. 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