Non-null references by default in the Java modeling language
Bibliographic record
Abstract
Based on our experiences and those of our peers, we hypothesized that in Java code, the majority of declarations that are of reference types are meant to be non-null. Unfortunately, the Java Modeling Language (JML), like most interface specification and object-oriented programming languages, assumes that such declarations are possibly-null by default. As a consequence, developers need to write specifications that are more verbose than necessary in order to accurately document their module interfaces. In practice, this results in module interfaces being left incompletely and inaccurately specified. In this paper we present the results of a study that confirms our hypothesis. Hence, we propose an adaptation to JML that preserves its language design goals and that allows developers to specify that declarations of reference types are to be interpreted as non-null by default. We explain how this default is safer and results in less writing on the part of specifiers than null-by-default. The paper also reports on an implementation of the proposal in some of the JML tools.
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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 | 0.047 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".