FeaMod: Enhancing Modularity, Adaptability and Code Reuse in Embedded Software Development
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".