Adapting the User Interface of Integrated Development Environments (IDEs) for Novice Users.
Bibliographic record
Abstract
The usability of a user interface is often neglected in the design and development of software applications.An Integrated Development Environment (IDE) is prone to poor usability problems due to the rich functionality offered through its User Interface (UI).Since an IDE targets a wide range of users (from novice to expert users), the usability requirement for an IDE vary considerably.Novice users, such as first year undergraduate students, often have difficulty in understanding many of the features provided in an IDE and have a hard time locating the appropriate menu elements.We propose an Adaptive User Interface (AUI) architecture which provides a simplified UI for the Eclipse IDE.The AUI assists novice users in using complex IDEs.We develop adaptive algorithms that modify the existing menu system for the Eclipse IDE based on statistical user interaction patterns.Our adaptive algorithms perform a cost-benefit analysis when modifying the menu system.The algorithms determine the optimal changes which reduce the time needed by novice users when searching for menu elements.A prototype AUI is developed as an Eclipse plug-in for novice users of the Eclipse IDE.Through an initial case study, we demonstrate the benefits of our AUI in improving the usability of the Eclipse IDE.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".