A Practical Framework for the Dataflow Pointcut in AspectJ
Bibliographic record
Abstract
In this paper, we present the design and the implementation of the dataflow pointcut in AspectJ compiler ajc 1.5.0. Some security concerns are sensitive to flow of information in a program execution. The dataflow pointcut has been proposed by Masuhara and Kawauchi in order to easily implement such security concerns in aspect-oriented programming languages. The pointcut identifies join points based on the origins of values. The dataflow pointcut can detect and fix a lot of vulnerabilities that result from not validating input effectively, e.g., Web application vulnerabilities, process injection, log forging, and path injection. AspectJ extends the Java programming language to implement crosscutting concerns modularly in general. The implementation methodology of the dataflow pointcut which depends in define-use analysis is described in detail together with case studies that demonstrate how the implemented dataflow pointcut can detect a considerable number of vulnerabilities.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".