FeaMod: Enhancing Modularity, Adaptability and Code Reuse in Embedded Software Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it