Reducing the use of nullable types through non-null by default and monotonic non-null
Bibliographic record
Abstract
With Java 5 annotations, the authors note a marked increase in tools that can statically detect potential null dereferences. To be effective, such tools require that developers annotate declarations with nullity modifiers and have annotated API libraries. Unfortunately, in the experience of the authors, specifying moderately large code bases, the use of non-null annotations is more labour intensive than it should be. Motivated by this experience, the authors conducted an empirical study of five open source projects totalling 700K lines-of-code, which confirms that, on average, 75% of reference declarations are meant to be non-null, by design. Guided by these results, the authors propose the adoption of non-null-by-default semantics. This new default has advantages of better matching general practice, lightening developer annotation burden and being safer. The authors also describe the Eclipse Java Modelling Language (JML) Java Development Tooling (JDT), a tool supporting the new semantics, including the ability to read the extensive API library specifications written in the JML. Issues of backwards compatibility are addressed. In a second phase of the empirical study, the authors analysed the uses of null and noted that over half of the nullable field references are only assigned non-null values. For this category of reference, the authors introduce the concept of monotonic non-null type and illustrate the benefits of its use.
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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 | 0.045 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".