Scaling an object-oriented system execution visualizer through sampling
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".