Bibliographic record
Abstract
Software developers often confront questions such as "Why was the code implemented this way"? To answer such questions, developers make use of information in a software system's bug and source repositories. In this paper, we consider two user interfaces for helping a developer explore information from such repositories. One user interface, from Holmes and Begel's Deep Intellisense tool, exposes historical information across several integrated views, favouring exploration from a single code element to all of that element's historical information. The second user interface, in a tool called Rationalizer that we introduce in this paper, integrates historical information into the source code editor, favouring exploration from a particular code line to its immediate history. We introduce a model to express how software repository information is connected and use this model to compare the two interfaces. Through a lab experiment, we found that our model can help predict which interface is helpful for a particular kind of historical question. We also found deficiencies in the interfaces that hindered users in the exploration of historical information. These results can help inform tool developers who are presenting historical information either directly from or mined from software repositories.
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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 | 0.006 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".