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Continuous SBOM Generation for Development Workflows: An Empirical Comparison with Build-Time Approaches and Runtime Dependency Detection
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2025 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Software Bill of Materials (SBOM) have become essential for software supply chain security, driven by regulatory mandates and increasing supply chain attacks. Traditional SBOM generation occurs at build time, creating static snapshots that quickly become outdated during active development and fail to capture runtime-loaded dependencies. This thesis addresses the critical gap between build-time accuracy and development-time security feedback by investigating continuous SBOM generation approaches integrated into developer workflows.

This research employs Design Science Research methodology to develop and evaluate a continuous SBOM generation plugin for the Node.js ecosystem. The artifact integrates with Visual Studio Code to provide real-time dependency monitoring, automatic SBOM regeneration, and immediate vulnerability feedback during development activities. We conducted a systematic three-sprint empirical evaluation comparing continuous generation against established static approaches (Syft, CDXGen, Trivy) across direct dependencies, transitive dependencies, and runtime detection scenarios.

Our empirical findings demonstrate that continuous SBOM generation maintains equivalent accuracy for direct dependencies (100% detection rate) while providing superior component discovery (8.8% increase, 1,674 vs 1,539 components) and significantly faster vulnerability feedback (12.8 seconds vs 5-15 minutes for CI pipelines). However, continuous generation exhibits reduced transitive dependency coverage (87.5% vs 95.3% for best static tools) and potential scalability limitations including memory accumulation patterns (23% increase across test runs). Runtime dependency detection achieves excellent discovery rates (100% for dynamic and environment[1]specific dependencies) but suffers from severe metadata completeness degradation (10-42% version coverage) that limits practical deployment for compliance or com[1]prehensive security assessment.

The research contributes the first systematic empirical comparison of continuous vs. static SBOM generation methodologies, providing evidence-based guidance for tool selection and deployment strategies. We demonstrate that continuous generation is most suitable for small-to-medium development projects requiring immediate security feedback, while enterprises should maintain hybrid approaches combining continuous generation for development environments with static generation for comprehensive compliance coverage. Technical limitations including scale constraints (evaluation limited to 22 packages), ecosystem specificity (Node.js only), and unknown enterprise performance characteristics constrain generalizability and indicate clear directions for future research.

This work provides a foundation for informed decision-making in enterprise SBOM implementation while identifying specific technical and methodological challenges that require continued research investment to achieve comprehensive software supply chain security.

Place, publisher, year, edition, pages
2025. , p. 59
Keywords [en]
Software Bill of Materials (SBOM), Continuous Integration, Software Supply Chain Security, Dependency Management, Runtime Detection, Vulnerability Management
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-28287OAI: oai:DiVA.org:bth-28287DiVA, id: diva2:1980821
Subject / course
PA2534 Master's Thesis (120 credits) in Software Engineering
Educational program
PAADA Master Qualification Plan in Software Engineering 120,0 hp
Supervisors
Examiners
Available from: 2025-07-03 Created: 2025-07-02 Last updated: 2025-09-30Bibliographically approved

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