Bibliographic record
Abstract
This paper presents a new, inexpensive, mechanism for constructing a complete call graph for Java programs at runtime, and provides an example of using the mechanism for implementing a dynamic reachability-based interprocedural analysis (IPA), namely dynamic XTA. Reachability-based IPAs, such as points-to analysis and escape analysis, require a context-insensitive call graph of the analyzed program. Computing a call graph at runtime presents several challenges. First, the overhead must be low. Second, when implementing the mechanism for languages such as Java, both polymorphism and lazy class loading must be dealt with correctly and efficiently. We propose a new, low-cost, mechanism for constructing runtime call graphs in a JIT environment. The mechanism uses a profiling code stub to capture the first execution of a call edge, and adds at most one more instruction to repeated call edge invocations. Polymorphism and lazy class loading are handled transparently. The call graph is constructed incrementally, and it supports optimistic analysis and speculative optimizations with invalidations. We also developed a dynamic, reachability-based type analysis, dynamic XTA, as an application of runtime call graphs. It also serves as an example of handling lazy class loading in dynamic IPAs. The dynamic call graph construction algorithm and dynamic version of XTA have been implemented in Jikes RVM. We present empirical measurements of the overhead of call graph profiling and compare the characteristics of call graphs built using our profiling code stubs with conservative ones constructed by using dynamic class hierarchy analysis (CHA).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".