Investigating the relationship between usability and conceptual gaps for human-centric CASE tools
Bibliographic record
Abstract
Several interviews that we conducted highlight that many of the ease-of-use (usability) problems of CASE tools are instances of "conceptual gaps". A conceptual gap arises because of some difference between the software developer's mental model of the integrated development environment (IDE) and the way it can be used. Filling these gaps is the first step towards human-centric IDE. In this article, we begin by motivating our investigations with a survey highlighting common usability problems in the most popular Java IDEs. We then discuss how the developer's experiences with the complicity of cognitive studies can minimize these conceptual gaps while making the IDE more human-centered. We close our discussion with recommendations for establishing a rigorous scientific investigation for filling these conceptual gaps, as well as for developing and evaluating the ease of use of IDEs.
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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 | 0.166 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".