Proof Linking: A Modular Verification Archietcture for Mobile Code Systems
Bibliographic record
Abstract
This dissertation presents a critical rethinking of the Java bytecode verification architecture from the perspective of a software engineer. In existing commercial implementations of the Java Virtual Machine, there is a tight coupling between the dynamic linking process and the bytecode verifier. This leads to delocalized and interleaving program plans, making the verifier difficult to maintain and comprehend. A modular mobile code verification architecture, called Proof Linking, is proposed. By establishing explicit verification interfaces in the form of proof obligations and commitments, and by careful scheduling of linking events, Proof Linking supports the construction of bytecode verifier as a separate engineering component, fully decoupled from Java's dynamic linking process. This turns out to have two additional benefits: (1) Modularization enables distributed verification protocols, in which part of the verification burden can be safely offloaded to remote sites; (2) Alternative static analyses can now be integrated into Java's dynamic linking process with ease, thereby making it convenient to extend the protection mechanism of Java. These benefits make Proof Linking a competitive verification architecture for mobile code systems. A prototype of the Proof Linking Architecture has been implemented in an open source Java Virtual Machine, the Aegis VM (http : //aegisvm. sourcef orge .net). On the theoretical side, the soundness of Proof Linking was captured in three correctness conditions: Safety, Monotonicity and Completion. Java instantiations of Proof Linking with increasing complexity have been shown to satisfy all the three correctness conditions. The correctness proof had been formally verified by the PVS proof checker.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".