  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).
