Building Secure Software for Smart Aging Care Systems: An Agile Approach
Bibliographic record
Abstract
There exists a persistent challenge in sufficiently addressing software security issues and effectively integrating security procedures into the software development life cycle. Software products vulnerable to security threats can result in severe consequences, especially in sensitive domains like those providing age-related support for older adults. This work offers guidelines to address software vulnerabilities in one of such evolving and sensitive domains, namely, Smart Aging Care Systems (SACS). The existing guidelines for securing the software cannot effectively address the observed vulnerabilities in SACS because of the unique demographics of its users and special design requirements. Therefore, the primary objective of this paper is to enhance the comprehension of secure software development methods, considering best security practices or controls in general and tailoring their selection based on the unique requirements of SACS. The chosen controls are then reshaped to align with the specific needs of SACS, with implementation carried out using the agile framework, specifically Scrum. We believe that this work will aid software development organizations in significantly enhancing the security of their software products for SACS dynamically and effectively, leveraging the Scrum framework, and also inspire its implementation in other emerging domains.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".