A PRISMA-Driven Bibliometric Analysis of the Scientific Literature on Assurance Case Patterns

TL;DR

The paper examines how assurance case patterns are developed and reused to support safety, security, and ethics in mission-critical systems. Using a SEGRESS adapted PRISMA methodology, it analyzes 92 ACP studies from two decades to map publication trends, venues, and collaboration structures, while identifying gaps and future directions. Key findings include a peak ACP activity around 2014, SAFECOMP as the leading venue, a UK-US preponderance in affiliations, and emerging themes around ML ethics, security, and model-based assurance. The work provides a data-driven view of ACP evolution and offers guidance for future research, including leveraging AI to automate ACP instantiation and addressing fairness, bias, and explainability in ML-enabled systems.

Abstract

Justifying the correct implementation of the non-functional requirements (e.g., safety, security) of mission-critical systems is crucial to prevent system failure. The later could have severe consequences such as the death of people and financial losses. Assurance cases can be used to prevent system failure, They are structured arguments that allow arguing and relaying various safety-critical systems' requirements extensively as well as checking the compliance of such systems with industrial standards to support their certification. Still, the creation of assurance cases is usually manual, error-prone, and time-consuming. Besides, it may involve numerous alterations as the system evolves. To overcome the bottlenecks in creating assurance cases, existing approaches usually promote the reuse of common structured evidence-based arguments (i.e. patterns) to aid the creation of assurance cases. To gain insights into the advancements of the research on assurance case patterns, we relied on SEGRESS to conduct a bibliometric analysis of 92 primary studies published within the past two decades. This allows capturing the evolutionary trends and patterns characterizing the research in that field. Our findings notably indicate the emergence of new assurance case patterns to support the assurance of ML-enabled systems that are characterized by their evolving requirements (e.g., cybersecurity and ethics).

Figures (13)

• Figure 1: A sample safety case (adapted from b49).
• Figure 2: A sample assurance case pattern (adapted from b61).
• Figure 3: Methodology framework for our bibliometric analysis
• Figure 4: PRISMA flow diagram illustrating the identification and selection of primary studies
• Figure 5: Publication-year distribution