ACCOMMODATING USABILITY DRIVEN CHANGES IN EXISTING SOFTWARE ARCHITECTURE
Bibliographic record
Abstract
Issues such as whether a product is easy to learn, to use, and whether the user can efficiently complete tasks using it, may greatly affect a product’s acceptance in the marketplace. In software engineering, the support for usability is widely believed to be independent of software architecture design. This belief stems from the efforts to separate the user interface component from the application’s internal logic, thus enabling changes to the interface without affecting the software architecture. This assumption has been recently challenged by Bass et al. [6] who argue that architectural patterns must be in place in order to support good usability design. We investigate the degree to which this revised belief is true with a case study on the redesign of an existing application for better supporting usability. The redesign is based on implementing a task-oriented interface and help system. Preliminary results show that much, though not all, of the required changes can be done without major changes to the software architecture of GIMP. The results do support the idea that the envisioned usability redesign could not easily be implemented without some powerful architectural features that do not necessarily correspond to the specific patterns identified by Bass et al.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".