Compression techniques to simplify the analysis of large execution traces
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
Dynamic analysis consists of analyzing the behavior of a software system to extract its properties. There have been many studies that use dynamic information to extract high-level views of a software system or simply to help software engineers to perform their daily maintenance activities more effectively. One of the biggest challenges that such tools face is to deal with very large execution traces. By analyzing the execution traces of the software systems we are working on, we noticed that they contain many redundancies that can be removed. This led us to create a comprehension-driven compression framework that compresses the traces to make them more understandable. In this paper, we present and explain its components. The compression framework is reversible that is the original trace can be reconstructed from its compressed version. In addition to that, we conducted an experiment with the execution traces of two software systems to measure the gain attained by such compression.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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