Bibliographic record
Abstract
Comparing program analysis results from different static and dynamic analysis tools is difficult and therefore too rare, especially when it comes to qualitative comparison. Analysis results can be strongly affected by specific details of programs being analyzed, so quantitative evaluation should be supplemented by qualitative identification of those details. Our general aim is to develop tools to reduce the difficulty of qualitative comparison. In this paper, we focus on comparison of call graphs in particular. We present two complementary tools for comparing call graphs. Our main contribution is a call graph difference search tool that ranks call graph edges by their likelihood of causing large differences in the call graphs. This is complemented by a simple interactive call graph viewer that highlights specific differences between call graphs, and allows a user to browse through them. In a search for the causes of call graph differences, a user first uses the search tool to identify which of the thousands of spurious edges to look at more closely, and then uses the interactive viewer to determine in detail the root cause of a difference. We present the ranking algorithm used in the difference search tool. We also report on a case study using the comparison tools to determine the most important sources of imprecision in a typical static call graph by comparing it to a dynamic call graph of the same benchmark.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".