Intelligent Automation of Trace Identification Processes for Complex Mission Critical Robotics Systems

Achieving trustworthy and trusted autonomy starts early in the development process through performing hazard analysis, fault analysis, identifying mitigating requirements, and carefully and rigorously tracing those requirements all the way into design, models, implementation, and tests. However, traceability can be time-consuming, costly, and error-prone. To address these challenges, significant advances in automated traceability have used deep learning technologies to generate highly accurate trace links between software artifacts such as requirements, design, code, and test cases across many different system domains.nbsp; These links are then used to support requirements analysis tasks such as safety analysis, requirements satisfaction analysis, and to support requirements reuse.nbsp; However,nbsp; automated traceability underperforms in highly technical domains with terminology that differs significantly from the publicly available training data.nbsp; The aim of this project is to address this problem by delivering a reliable and fully-automated solution for constructing, training, and validating a domain-specific language model that performs automated software and systems traceability tasks at high degrees of accuracy for the targeted domain.nbsp; The language model is used to perform and augment a variety of requirements analysis tasks including change impact analysis, safety analysis, requirements reuse, and requirements analysis in order to design and deploy more reliable software systems. The approach will be applied to complex, mission-critical robotics problems in order to increase the rigor of developing robotics systems, especially those where achieving trustworthy and trusted autonomy is paramount. The traceability and requirements analysis solutions will be delivered through the highly interactive and visual SAFA tool.