Adapting the User Interface of Integrated Development Environments (IDEs) for Novice Users.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it