Requirements engineering-driven collaborative software maintenance framework for embedded systems using continual learning
| dc.contributor.advisor | Alwidian, Sanaa | |
| dc.contributor.advisor | Azim, Akramul | |
| dc.contributor.author | Fariha, Asma | |
| dc.date.accessioned | 2024-06-17T16:34:56Z | |
| dc.date.available | 2024-06-17T16:34:56Z | |
| dc.date.issued | 2024-04-01 | |
| dc.degree.discipline | Electrical and Computer Engineering | |
| dc.degree.level | Master of Applied Science (MASc) | |
| dc.description.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. | |
| dc.description.sponsorship | University of Ontario Institute of Technology | |
| dc.identifier.uri | https://ontariotechu.scholaris.ca/handle/10155/1779 | |
| dc.language.iso | en | |
| dc.subject.other | Requirement engineering | |
| dc.subject.other | Embedded system | |
| dc.subject.other | Software maintenance | |
| dc.subject.other | Anomaly detection | |
| dc.subject.other | Continual learning | |
| dc.title | Requirements engineering-driven collaborative software maintenance framework for embedded systems using continual learning | |
| dc.type | Thesis |