Scaling an object-oriented system execution visualizer through sampling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasingly, applications are being built by combining existing software components. For the most part, a software developer can treat the components as black-boxes. However, for some tasks, such as when performance tuning, a developer must consider how the components are implemented and how they interact. In these cases, a developer may be able to perform the task more effectively by using dynamic information about how the system executes. In previous work, we demonstrated the utility of a tool, called AVID (Architectural VIsualization of Dynamics), that animates dynamic information in terms of developer-chosen architectural views. One limitation of this earlier work was that AVID relied on trace information collected about the system's execution; traces for even small parts of a system's execution can be enormous, limiting the duration of execution that can be considered. To enable AVID to scale to larger longer-running systems, we have been investigating the visualization and animation of sampled dynamic information. In this paper, we discuss the addition of sampling support to AVID, and we present two case studies in which we experimented with animating sampled dynamic information to help with performance tuning tasks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it