FeaMod: Enhancing Modularity, Adaptability and Code Reuse in Embedded Software Development

The increasing prevalence of embedded systems in Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has amplified the necessity for effective and adaptable software development practices. The challenges encountered in designing and developing these systems stem from the requirement to efficiently integrate advanced computational paradigms like machine learning and fog computing. Their inherent complexity and rigidity often limit the systems’ adaptability to evolving requirements and complicate the effective management of feature dependencies, versioning, customization, and configuration in distributed environments. To address these challenges, we propose the FeaMod framework, integrating feature-based modularity with adaptive feature modeling for enhanced efficiency in embedded software design. Using the Bidirectional Encoder Representations from Transformers (BERT) model, FeaMod employs automated feature extraction through advanced static code analysis, facilitating the identification of computational features and requirements from existing codebases. These features are encapsulated in an adaptive feature model (AFM) that encourages code reuse and allows for dynamic configuration and system integration. By introducing a set of rules governing feature relationships, our approach ensures the adaptive nature of the model, enhancing its flexibility in response to changing system requirements, user preferences, and varying environmental conditions.