Lost in Transmission: An Information-Theoretic Account of Unsupervised Software Traceability

Traceability remains a critical capability to ensure system reliability, maintainability, and compliance in modern software development. 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. Our approach leverages self-information and mutual information (MI) to quantify the informativeness and alignment of source and target artifacts. Through a comprehensive empirical analysis of industry datasets, we show that typical traceability corpora exhibit significant information imbalances, where the source code contains on average more information than the corresponding documentation. In addition, the observed levels of mutual information, loss, and noise reveal inherent constraints on the ability of unsupervised techniques to recover accurate trace links. These findings suggest that improving traceability in practice requires a shift to data-centric engineering, focusing on artifact quality, consistency, and information alignment; rather than solely advancing model sophistication (or complexity). Our results provide insights for practitioners to better assess traceability readiness and guide improvements in documentation and development workflows.