传输中迷失：无监督软件可追溯性的信息论解释
Lost in Transmission: An Information-Theoretic Account of Unsupervised Software Traceability
AI总结：
研究无监督软件可追溯性，引入信息论框架TraceXplainer，利用自信息和互信息量化工件信息及对齐，经行业数据集实证分析，揭示信息不平衡与技术约束，建议转向以数据为中心的工程，为从业者提供评估及改进见解。
摘要：
可追溯性对于确保现代软件开发中的系统可靠性、可维护性和合规性至关重要。尽管无监督信息检索和机器学习技术被广泛用于自动追溯链接恢复，但其有效性常受基础工件质量和结构限制。在实际工业环境中，这些方法所基于的假设往往不成立。传统评估指标在未明确考虑数据特征时会误判性能。为此引入TraceXplainer框架，利用自信息和互信息量化工件信息及对齐情况。通过对行业数据集的实证分析发现，典型可追溯性语料库存在信息不平衡，无监督技术恢复准确追溯链接存在固有约束。这表明实践中提高可追溯性需转向以数据为中心的工程，关注工件质量、一致性和信息对齐，而非仅提升模型复杂度。研究结果为从业者评估可追溯性准备情况及改进文档和开发工作流程提供了见解。
英文摘要：
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.
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