Interactive usability instrumentation
Bibliographic record
Abstract
Usage data logged from user interactions can be extremely valuable for evaluating software usability. However, instrumenting software to collect usage data is a time-intensive task that often requires technical expertise as well as an understanding of the usability issues to be explored. We have developed a new technique for software instrumentation that removes the need for programming. Interactive Usability Instrumentation (IUI) allows usability evaluators to work directly with a system's interface to specify what components and what events should be logged. Evaluators are able to create higher-level abstractions on the events they log and are provided with real-time feedback on how events are logged. As a proof of the IUI concept, we have created the UMARA system, an instrumentation system that is enabled by recent advances in aspect-oriented programming. UMARA allows users to instrument software without the need for additional coding, and provides tools for specification, data collection, and data analysis. We report on the use of UMARA in the instrumentation of two large open-source projects; our experiences show that IUI can substantially simplify the process of log-based usability evaluation.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".