A comparative evaluation of heuristic-based usability inspection methods
Bibliographic record
Abstract
Given that heuristic evaluation (HE) is a popular evaluation method among practitioners despite criticisms surrounding its performance and reliability, there is a need to improve the method's performance. Several studies have shown HE-Plus, an emerging variant of HE, to outperform HE in both effectiveness and reliability. HE-Plus uses the same set of heuristics as HE; the only difference between these two methods is the 'usability problems profile' element in HE-Plus. This paper reports our attempt to verify the original profile employed in HE-Plus based on usability problem classification in the User Action Framework and an experiment evaluating its outcome by comparing HE with two HE variants using a profile (HE-Plus and HE++) and a control group. Our results confirmed the role of the 'usability problems profiles' on improving the performance and reliability of heuristic evaluation: both HE-Plus and HE++ outperformed HE in terms of effectiveness as well as reliability.
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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 | 0.053 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".