Bibliographic record
Abstract
When modifying or debugging a software system, among other tasks, developers must often understand and manipulate source code that crosscuts the system's structure. These tasks are made more difficult by limitations of the two approaches currently used to present details of crosscutting structure: tree views and structural diagrams. Tree views force the developer to manually synthesize information from multiple views; structure diagrams quickly suffer from graphical complexity. We introduce an active model as a means of presenting the right information about crosscutting structure to a developer at the right time. An active model is produced as a result of three automated operations---projection, expansion, and abstraction. Combined with particular user interaction features during display, these operations enable a view of the model to be presented to the developer without suffering from the complexity of existing approaches. We have implemented an active model tool, called ActiveAspect, for presenting crosscutting structure described by AspectJ aspects. We report on the results of a case study in which the tool was used effectively by two subjects to implement a modification task to a non-trivial AspectJ system.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".