Bibliographic record
Abstract
Implementing software development tools as integrated development environment (IDE) plugins gives tools direct access to a range of useful representations of the program being created and can improve programmer efficiency. These benefits must be weighed against the effort to integrate the tool into the IDE, effort which may need to be repeated for each IDE targeted. In this paper, we introduce Fishtail, a prototype plugin for the Eclipse IDE, which assists programmers in discovering code examples and documentation on the web relevant to their current task. Fishtail uses a detailed history of programmer interactions with the source code to automatically determine relevant web resources. We describe the key factors that make it attractive to implement Fishtail as a plugin, and the requirements Fishtail imposes on the plugin/IDE interface. To reach a broader user base and understand how well our tool supports different programming styles and IDE architectures, we have recently begun investigating how to make a version of Fishtail available in the Visual Studio IDE. We outline some of the challenges we face in trying to reuse code from the original Eclipse plugin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.085 |
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".