ιDOT: a DOT calculus with object initialization
Bibliographic record
Abstract
The Dependent Object Types (DOT) calculus serves as a foundation of the Scala programming language, with a machine-verified soundness proof. However, Scala's type system has been shown to be unsound due to null references, which are used as default values of fields of objects before they have been initialized. This paper proposes ιDOT, an extension of DOT for ensuring safe initialization of objects. DOT was previously extended to κDOT with the addition of mutable fields and constructors. To κDOT, ιDOT adds an initialization effect system that statically prevents the possibility of reading a null reference from an uninitialized object. To design ιDOT, we have reformulated the Freedom Before Commitment object initialization scheme in terms of disjoint subheaps to make it easier to formalize in an effect system and prove sound. Soundness of ιDOT depends on the interplay of three systems of rules: a type system close to that of DOT, an effect system to ensure definite assignment of fields in each constructor, and an initialization system that tracks the initialization status of objects in a stack of subheaps. We have proven the overall system sound and verified the soundness proof using the Coq proof assistant.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".