Alwidian, SanaaAzim, AkramulFariha, Asma2024-06-172024-06-172024-04-01https://ontariotechu.scholaris.ca/handle/10155/1779Embedded software post-deployment evolutions pose significant threats to the safety and reliability of embedded software if it is not adapted to software maintenance through requirements engineering. To solve this problem, we propose a collaborative framework that enables efficient requirements elicitation and continuously integrates it into maintenance. We designed a requirements forum to enhance elicitation through centralized stakeholder collaboration. This study investigated fault and failure detection in the maintenance phase with continual learning as a mechanism of incremental inclusion. The novel CNNBiLSTM deep-learning model on a public drone dataset outperformed state-of-the-art models, achieving a 100% true positive rate in three scenarios. On the other hand, we experienced a 14% increase in the recall metric for the replay-based method combined with pre-training compared to pre-training when fault detection requirements were integrated incrementally. Our findings support the idea that embedded software safety and security can be greatly enhanced through this collaborative framework.enRequirement engineeringEmbedded systemSoftware maintenanceAnomaly detectionContinual learningThesis