SoK: Systematizing Software Artifacts Traceability via Associations, Techniques, and Applications

2026-03-17

AI Summary

This study addresses the longstanding fragmentation in software artifact traceability research, characterized by incomplete linkages, ambiguous techniques, and disconnected application contexts. Through a systematic literature review, it constructs the first comprehensive traceability landscape encompassing 22 artifact types and 23 relationship kinds, and introduces a technology decision map, a standardized evaluation benchmark, and a role-oriented dynamic path alignment framework. The work uncovers critical challenges: a pervasive code-centric bias, a reproducibility crisis stemming from only 37% of studies releasing open-source artifacts, and a significant adoption gap with 95% of proposed tools never deployed in industry. In response, it offers targeted strategies to bridge these gaps, establishing a unified knowledge foundation for future research and practical implementation in traceability.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Solvers and Tools
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Security and Privacy: Data transparency and provenance

Abstract

Software development relies heavily on traceability links between various software artifacts to ensure quality and facilitate maintenance. While automated traceability recovery techniques have advanced for different artifact pairs, the field remains fragmented with an incomplete overview of artifact associations, ambiguous linking techniques, and fragmented knowledge of application scenarios. To bridge these gaps, we conducted a systematic literature review on software traceability recovery to synthesize the linked artifacts, recovery tools, and usage scenarios across the traceability ecosystem. First, we constructed the first global artifacts traceability graph of 23 associations among 22 artifact types, exposing a severe research imbalance that heavily favors code-related links. Second, while recovery techniques are shifting toward deep semantic models, a reproducibility crisis persists (e.g., only 37% of studies released code); to address this, we provided a comprehensive evaluation framework including a technical decision map and standardized benchmarks. Finally, we quantified an industrial adoption gap (i.e., 95% of tools remain confined to academia) and proposed a role-centric framework to dynamically align artifact paths with concrete engineering activities. This review contributes a coherent knowledge framework for artifacts traceability research, identifies current trends, and provides directions for future work.

Problem

• software traceability
• artifact associations
• traceability recovery
• software artifacts
• systematic literature review

Innovation

• software traceability
• artifact association graph
• reproducibility framework
• role-centric alignment
• systematic literature review