A standards‐based model of system maintainability requirements
Bibliographic record
Abstract
SUMMARY The nonfunctional requirements (NFR) are often captured only generically at a fairly high level, and they do not include the levels of detail necessary at this stage for the system engineers to allocate them as specific functionalities to be handled either by the software or the hardware, or a specific combination of the two. The European Cooperation for Space Standardization (ECSS) series of standards for the aerospace industry includes maintainability requirements as one of 16 types of NFR for embedded and real‐time software. A number of maintainability‐related concepts are dispersed throughout the ECSS, ISO 9126, and Institute of Electrical and Electronics Engineers standards to describe, at varying levels of detail, the various types of candidate maintainability requirements at the system, software, and hardware levels. This paper organizes these dispersed maintainability concepts into a standards‐based reference model of system maintainability requirements. The availability of this reference model can facilitate the early identification of the system maintainability‐NFR and their detailed allocation as specific maintainability functions to be handled by the specified allocation to hardware or software, or a specific combination of the two. In the absence of such a reference model, these NFR are typically handled in practice much later on in the software development life cycle, when at system testing time, users and developers find out that a number of maintainability requirements have been overlooked and additional effort has to be expended to implement them. The approach adopted in this research for the structure of this reference NFR model is based on the generic model of software functional requirements proposed in the COSMIC – ISO 19761 model, thereby allowing the functional size of such maintainability requirements allocated to software to be measured. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".