Towards Requirements Specification Collaboration Forum for Embedded Software Systems
Bibliographic record
Abstract
Effective requirements specification for embedded software systems relies heavily on the collaboration between stakeholders to articulate accurate functional and non-functional requirements. At present, requirement engineers manually coordinate the sophisticated task of correct stakeholders selection for requirements and compilation of feedback in the embedded software domain. Because embedded software has an extensive number of stakeholders, the absence of an efficient collaboration platform causes a prolonged project completion time, elevated maintenance costs, or project failure. In this preliminary research paper, a stakeholder collaboration platform is proposed using an auto-encoder-based recommender system and SysML modeling language for embedded software systems. In the proposed framework, forums are the collaboration space for stakeholders to contribute to requirements analysis and specifications and are generated from the requirements diagram of the SysML modeling language. Owners of the requirements will be directly assigned to the forum from the SysML requirement profile information. To ensure adequate stakeholder engagement in the requirements specification process, the Collaborative Denoising Auto-Encoder (CDAE) recommender system is used for advanced auto-recommendations of requirement forums to stakeholders. This approach facilitates feedback collection and analysis to refine requirements specifications. The automatic forum creation and the advanced recommendation process of this framework will add no overhead to the requirements engineers or analysts; at the same time, a centralized collaboration platform for the stakeholders will save requirements analysis time and avoid future conflicts.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".