Bringing usability to the early stages of software development
Bibliographic record
Abstract
Usability has been increasingly recognized as an important factor in the acceptance of systems by end users. Usability requirements can be considered to be requirements that capture the usability goals and associated measures for a system under development. In order to ensure usable systems we must ensure identification of appropriate requirements regarding these critical aspects of systems. There is a basic need for systematic approaches to reason, model and analyze usability from the early stages of the software development. Furthermore, it is necessary to develop a usable ontology or classification of measurable aspects of usability that can be used to aid in the specification of usability requirements. These ontologies should be represented in a way that facilitates their use as guidelines for the requirements elicitation process. We build on review of literature in the area of human-computer interaction and of usability engineering in developing a catalog of aspects of usability that can be considered during requirements gathering. This catalogue is used to guide the requirements engineer through alternatives for achieving usability. The approach is based on the use of the i* framework, having usability modeled as a special type of goal.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".