Multi-View Software Architecture Design: Case Study of a Mission-Critical Defense System
Bibliographic record
Abstract
As the use of software intensive systems increase in our daily lives, the systems are getting larger and larger every day. A significant portion of these software systems in use are medium to large in size and complexity. Most of them are also becoming a part of a system of systems. In addition to satisfying of a set of functional requirements, the users expect a certain level of quality in these systems. Quality requirements are also referred as nonfunctional requirements. Adequate satisfaction of quality requirements can be achieved via a careful software architecture design. Since the quality requirements are multi-faceted, the software architects have to consider many diverse aspects and provide a software architecture solution that can optimally satisfy both functional and nonfunctional requirements. Therefore, such a solution requires a multi-view software architecture design as the result of a careful systematic system development effort. Research reports with detailed case studies are helpful in bridging the gap between academia and industry. Research studies including a carefully designed real-world sample case studies will help practitioners and engineers to understand the theoretical concepts and apply the novel research findings in their practices. Therefore, in this study, we explain the systematic multi-view software architecture design with the help of a detailed mission-critical defense system development case study. The mission critical system in the case study is a Mine Neutralization System for navy mine hunting ships. In the study, we explain the multi-view software system architecture design step by step starting with identifying the system context, requirements, constraints, and quality expectations. We further outline the strategies, techniques, designs, and rationales used to satisfy a diverse set of requirements with a particular software architecture pattern. In addition, we introduce a novel architectural style named as a star-controller architectural style. We explain the use of the style with a related discussion.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".