bidirectional requirements traceability

Prior research on requirements traceability has largely overlooked auxiliary artifacts—intermediate artifacts that are neither source nor target requirements but influence trace link quality. Method: We conducted a systematic mapping study (SMS), rigorously screening, coding, and classifying 110 primary studies. Contribution/Results: This work presents the first comprehensive taxonomy of auxiliary artifacts, identifying 49 distinct types and 13 usage scenarios. It reveals their implicit yet pervasive impact on trace link quality—demonstrating their prevalence in industrial and academic practice. By uncovering this previously neglected dimension, the study fills a critical gap in traceability quality research and establishes an empirically grounded foundation for improving traceability reliability through novel intervention points targeting auxiliary artifacts.

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.

This study addresses the challenges of traceability in software engineering—stemming from fine-grained artifacts, heterogeneity of work products, and ambiguous responsibilities—by proposing Taxonomic Trace Links (TTL) as a complementary mechanism to traditional trace links. TTL leverages domain ontologies and taxonomies, integrated with automated classifiers, to establish early and structured traceability relationships among requirements, business use cases, and test cases. Empirical validation in an industrial case study at Ericsson demonstrates that TTL effectively supports traceability in real-world settings; however, its deployment is constrained by limitations in classifier accuracy and the complexity of ontology construction. The feasibility and applicability boundaries of TTL are rigorously assessed through a mixed-methods approach combining quantitative link evaluation with qualitative feedback from focus groups.

This work addresses the limitations of existing automated requirement traceability link recovery approaches, which typically rely on substantial labeled data and achieve only modest accuracy—conditions rarely met in real-world scenarios where labeled data are scarce. To overcome this challenge, the paper proposes T-SimCSE, a novel method that leverages the unsupervised pre-trained language model SimCSE to compute semantic similarity between requirements and target artifacts. It further introduces a new specificity metric to re-rank candidate links, enabling the generation of high-precision top-K traceability links without any labeled data. Experimental results across ten public datasets demonstrate that T-SimCSE significantly outperforms state-of-the-art methods in both recall and mean average precision (MAP).

This study addresses the lack of empirical analysis on how quality defects in requirements documents affect the performance of automated trace link recovery (TLR). For the first time, it systematically annotates 28 types of requirements quality defects across 189 use cases from two datasets, evaluates five state-of-the-art TLR methods, and analyzes their performance impacts using both statistical significance and effect size measures. The findings reveal that specific defect types differentially influence TLR effectiveness: while certain defects significantly degrade performance, others unexpectedly enhance it. Beyond identifying key factors that either hinder or facilitate TLR accuracy, this work demonstrates that the choice of TLR method should be strategically tailored to the quality characteristics of the underlying requirements documentation.

This work addresses the fragility of traditional requirements traceability in safety-critical software development, where reliance on external documentation often leads to silent breakdowns as code, requirements, and tests evolve independently. To overcome this, the paper proposes internalizing traceability as an intrinsic property of code structure by introducing language-native “Traceable” elements that enable compile-time verification of bidirectional links among requirements, implementations, and tests. The approach integrates code generation, metadata embedding, and build-time validation into the development workflow, providing proactive traceability assurance. When requirement changes disrupt traceability chains, the system automatically triggers warnings or build failures, thereby preventing traceability decay and significantly enhancing maintainability and reliability throughout software evolution.

This work addresses the challenge of automatically recovering traceability links among software architecture documentation, models, and source code—a longstanding barrier to effective system maintenance and consistency assurance. To bridge this gap, we present the first end-to-end ecosystem for architecture-level traceability recovery, comprising a RESTful API supporting four distinct tracing pipelines, an interactive web-based frontend named TraceView, and TraceViz, an embedded visualization plugin for Visual Studio Code. The system integrates seamlessly into developer workflows through asynchronous task processing and caching optimizations, enabling intuitive exploration of traceability links directly within the IDE. All components are publicly deployed, and preliminary user studies indicate that TraceViz significantly enhances developers’ cognitive efficiency during software comprehension tasks.

This work addresses the problem of implementation drift in evolving distributed systems, where runtime behavior gradually deviates from the original design. To tackle this issue, the paper proposes a design conformance assessment method based on distributed tracing data. It introduces, for the first time in the domain of distributed systems, conformance checking techniques from process mining, leveraging runtime traces collected via the OpenTelemetry standard and automatically comparing them against behavioral models defined at design time to quantify their alignment. The key contribution lies in establishing persistent, monitorable conformance metrics that enable continuous, automated evaluation of deviations between system implementation and design. This approach is readily applicable to modern distributed systems widely adopting OpenTelemetry for observability.

This work addresses the challenge of ensuring consistency between natural language requirements and generated code, a limitation in existing approaches that rely on simplified instructions and lack explicit traceability mechanisms. To overcome this, the authors propose TraceDev, a multi-agent automated software development framework comprising five role-based agents—Requirements Refinement, Design, Development, Testing, and Validation—that collaboratively perform end-to-end code generation for complex use cases. The key innovation lies in the Validator agent, which constructs and maintains a heterogeneous traceability graph to explicitly link requirements, design models, and code, thereby enabling cross-artifact consistency assurance and structured contextual memory. Evaluated on the ETOUR and SMOS datasets, TraceDev achieves success rates of 53.63% and 56.82%, respectively, representing a maximum improvement of 340.80% over baseline methods and significantly outperforming current state-of-the-art approaches.