Requirements engineering-driven collaborative software maintenance framework for embedded systems using continual learning
Date
2024-04-01
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Abstract
Embedded 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.