Breaking the Bento Box: Accelerating Visual Momentum in Data-flow Analysis
Bibliographic record
Abstract
The bento-box user interface and tool integration paradigm dominates integrated development environments (IDEs). In this paradigm, tools project different information about a system in disjoint panes (boxes) of a window while integrating updates between them as needed. Although popular and functional, the bento-box paradigm has its drawbacks; previous research has shown that expert developers experience disorientation as they work in these environments. In this paper, we explore how context can be preserved for developers within the bento-box paradigm by introducing and experimenting with a tool named ReachHover. This tool supports the answering of common data-flow reachability questions, which have been previously shown to be difficult for developers to answer. To ensure ReachHover supported practical reachability questions of interest to developers, we conducted, and report on, a formative survey of 72 practicing developers about the type and frequency of reachability questions they encounter in their work. We then conducted, and report on, a controlled user study in which 20 practicing developers used ReachHover, finding that participants who used ReachHover answered questions involving visiting multiple files more correctly than those who used standard tooling, and that those developers better maintained context while determining their answers. These findings demonstrate the potential of introducing context-preserving user interfaces for tools within the standard bento-box paradigm of development environments, opening up new avenues for improved tool expression and adoption.
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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 | 0.008 | 0.040 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".