Usability through Software Design
Bibliographic record
Abstract
Over the past two decades, the HCI community has proposed specific features that software applications should include to overcome some of the most common usability problems. However, incorporating such usability features into software applications may not be a straightforward process for software developers who have not been trained in usability (i.e., determining when, how, and why usability features should been considered). We have defined a set of usability guidelines for software development to help software engineers incorporate particular usability features into their applications. In this paper, we focus on the software design artifacts provided by the guidelines. We detail the structure of the proposed design artifacts and how they should be used according to the software development process and software architecture used in each application. We have tested our guidelines in an academic setting. Preliminary validation shows that the use of the guidelines reduces development time, improves the quality of the resulting designs, and significantly decreases the perceived complexity of the usability features from the developers' perspective.
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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 | 0.068 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".