Exploring Traceability Techniques on Software Engineering: A Review and Future Directions

Abstract

This review explores innovative approaches to software traceability, emphasizing modeling techniques, tool integration, and future research directions. Software traceability, essential for tracking artifacts across development stages, enhances quality and stakeholder collaboration. The paper reviews methods such as formal models, graph-based approaches, blockchain solutions, and AI-assisted tools, comparing their advantages and limitations. Empirical studies show diverse strengths in scalability, usability, and accuracy, while highlighting challenges like standardization and stakeholder engagement. Future directions underscore the potential of integrating blockchain and artificial intelligence to develop holistic, scalable frameworks that address current limitations. Overall, emerging techniques promise to significantly advance traceability efficacy, with ongoing research needed to overcome practical challenges and improve adoption.