Beyond Assertions: Advanced Specification and Verification with JML and ESC/Java2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Many state-based specification languages, including the Java Modeling Language (JML), contain at their core specification constructs familiar to most undergraduates: e.g., assertions, pre- and postconditions, and invariants. Unfortunately, these constructs are not sufficiently expressive to permit formal modular verification of programs written in modern object-oriented languages like Java. The necessary extra constructs for specifying an object-oriented module include (perhaps the less familiar) frame properties, datagroups, and ghost and model fields. These constructs help specifiers deal with potential problems related to, for example, unexpected side effects, aliasing, class invariants, inheritance, and lack of information hiding. This tutorial paper focuses on JML’s realization of these constructs, explaining their meaning while illustrating how they can be used to address the stated problems. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it