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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".