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).

翻译：论证关键任务系统非功能需求（如安全性、安全性）的正确实现对于防止系统故障至关重要。系统故障可能造成严重后果，例如人员伤亡和经济损失。保证案例可用于防止系统故障，它们是结构化论证，能够广泛论证和传递各类安全关键系统的需求，并检查此类系统是否符合工业标准以支持其认证。然而，保证案例的创建通常是手动、易出错且耗时的。此外，随着系统演进，可能需要进行大量修改。为克服创建保证案例的瓶颈，现有方法通常提倡复用常见的结构化循证论证（即模式）以辅助保证案例的创建。为深入理解保证案例模式研究的进展，我们基于SEGRESS对过去二十年发表的92项主要研究进行了文献计量分析。这有助于捕捉该领域研究的演变趋势与特征模式。我们的研究结果尤其表明，为支持具有动态需求特征（如网络安全与伦理）的机器学习赋能系统的保证，新型保证案例模式正在涌现。