Bibliographic record
Abstract
Abstract Source Code Management (SCM) is a valuable tool in software development projects, providing the ability to store and retrieve past versions of source files. Comparison tools offer the capability to compare different files or versions, highlighting their differences. Most such tools are built around a two‐pane interface, with files displayed side by side. Such interfaces are inefficient in the use of horizontal screen space, and ineffective since duplication makes text more difficult to read, leaving most of the comparison burden to the user. In this article we introduce an innovative metaphor for comparison interfaces. Based on a single‐pane interface, common text is displayed only once with differences intelligently combined into a single text, improving readability. To further improve usability, additional features were developed: difference classification (additions, deletions, and modifications); fine level of granularity; special features to display modifications; and intelligent white space handling. A usability study conducted among 16 participants using real‐world samples demonstrated the interface adequacy: on average, participants were 60% faster in performing comparison tasks, while the quality of answers improved by almost 80% on our scale. After the experiment, the proposed interface was declared the favorite choice by all participants. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".