Reducing Instrumentation Overhead when Reverse-Engineering Object Interactions
Bibliographic record
Abstract
Reverse-engineering object interactions from source \ncode can be done through static, dynamic, or hybrid (static plus \ndynamic) analyses. In the latter two, monitoring a program and \ncollecting runtime information translates into some overhead \nduring program execution. Depending on the type of application, \nthe imposed overhead can reduce the precision and accuracy of \nthe reverse-engineered object interactions (the larger the overhead \nthe less precise or accurate the reverse-engineered interactions), \nto such an extent that the reverse-engineered interactions \nmay not be correct, especially when reverse-engineering a multithreaded \nsoftware system. One is therefore seeking an instrumentation \nstrategy as less intrusive as possible. In our past work, we \nshowed that a hybrid approach is one step towards such a solution, \ncompared to a purely dynamic approach, and that there is \nroom for improvements. In this paper, we uncover, in a systematic \nway, other aspects of the dynamic analysis that can be improved \nto further reduce runtime overhead, and study alternative \nsolutions. Our experiments show effective overhead reduction \nthanks to a modified procedure to collect runtime information.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".