Bibliographic record
Abstract
Dynamic program slicing is used in a variety of tasks, including program debugging and security analysis. Despite being extensively studied in the literature, the only dynamic slicing solution for Java programs that is publicly available today is a tool named JavaSlicer. Unfortunately, JavaSlicer only supports programs written in Java 6 or below and does not support multithreading. To address these limitations, this paper contributes a new dynamic slicing tool for Java, named Slicer4J. Slicer4J uses low-overhead instrumentation to collect a runtime execution trace; it then constructs a thread-aware, inter-procedural dynamic control-flow graph and uses the graph to compute the slice. To support slicing through Java framework methods and native code, Slicer4J relies on a set of pre-constructed data-flow summaries of the main framework methods. It also allows the users to further customize this set, adding user-defined methods when needed. We demonstrate the applicability of Slicer4J on ten benchmark and open-source Java programs, comparing it with JavaSlicer, and discuss how to use and extend the tool.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".