Towards Achieving Effective Traceability Management Between And Within Software Artifacts
Zaki Pauzi · 2025
Modern software systems are increasingly complex, making effective traceability (e.g., linking requirements, design, code, and tests) essential yet challenging. This thesis, “Towards Achieving Effective Traceability Management Between and Within Software Artifacts,” addresses these challenges by leveraging Natural Language Processing (NLP) and data-driven methods to improve traceability across diverse software artifacts. It begins with a systematic mapping study of 96 papers (2013–2021), identifying key barriers such as representation similarity, scalability, and explainability in NLP-based traceability. Building on these insights, the thesis introduces a novel meta-analysis framework combining domain-specific Word2Vec embeddings and ARIMA time series modeling to forecast emerging research trends. To enhance semantic understanding, a domain-centric approach is proposed, using embedding-based mapping to connect conceptual elements across heterogeneous artifacts. The thesis also investigates the evolution of design artifacts, particularly UML class diagrams in long-standing open-source projects, revealing patterns of “UML dormancy” and misalignment between design and implementation. Finally, the work bridges theory and practice through a multivocal mapping study and an industrial experience report, demonstrating the successful deployment of end-to-end traceability in a real-world DevOps environment. These findings offer actionable insights for improving traceability tools and processes in industry. Overall, this thesis contributes a comprehensive, multi-dimensional framework for advancing traceability in software engineering, promoting more maintainable, transparent, and sustainable software systems.