JML Support for Primitive Arbitrary Precision Numeric Types: Definition and Semantics.
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Bibliographic record
Abstract
The Java Modeling Language (JML) is a notation for specifying and describing the detailed design and implementation of Java modules.An important language design goal of JML has been to preserve the semantics of Java to the extent possible.Thus, in particular, Java numeric expressions have the same meaning in JML.We illustrate how such a semantics fails to match the expectations of specification authors and readers who generally think in terms of arbitrary precision arithmetic (rather than the fixed precision provided by Java).As a result, an unusually high number of published JML specifications are invalid or inconsistent, including cases from the security critical area of smart card applications.We briefly examine JML's ancestry and language design principles; this helps to explain the origin of the semantic gap between user expectations and the current meaning given to JML numeric expressions.With the objective of better matching user expectations we introduce JMLb, a variant of JML supporting primitive arbitrary precision numeric types as well as "math modes" to control the semantics of arithmetic expressions.This is done in a manner that is consistent with JML's language design goals.A semantics of JMLb expressions is given by means of an embedding into PVS.The problem presented here will arise in the design of most interface specification languages that must deal with, e.g., mathematical integers in specifications and their fix precision approximations in code.We examine how the problem may manifest itself in other languages (such as Eiffel, Spark and the UML/OCL-Java notation of the KeY project) and comment on the applicability of our solution.
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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 | 0.000 |
| 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 | 0.000 | 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 it