No Zombie Types: Liveness-Based Justification For Monotonic Gradual Types
Bibliographic record
Abstract
Gradual type systems with the monotonic dynamic semantics, such as HiggsCheck implementing SafeTypeScript, are able to achieve decent performance, making them a viable option for JavaScript programmers seeking run-time-checkable type annotations. \nHowever, the type restrictions for objects in the monotonic dynamic semantics are, as the name suggests, monotonic. \nOnce a typed reference is defined or assigned to refer to an object, the contract carrying the type obligation of the reference is part of the object for the remainder of execution. \nIn some cases, such contracts become "zombies": the reference that justifies a contract is out of scope, yet the object still retains the type obligation. \n \nIn this thesis, we propose a novel idea of contract liveness and its implementation. \nBriefly speaking, contracts must be justified by live stack references defined with associated type obligations. \nOur implementation, taking inspiration from how garbage collectors approximate object liveness by reachability of objects, approximates contract liveness by reachability of contracts. \nThen, to achieve a much closer approximation to contract liveness, we introduce a poisoning process: \nwe nullify the stack references justifying the violated contract, and associate the location that triggered the contract violation with a poisoned reference for blame. \n \nThe implementation is compared with the original implementation of HiggsCheck. The comparison shows our system is fully compatible with code that raised no errors, with a small performance penalty of 8.14% average slowdown. \nWe also discuss the performance of the contract removal process, and possible worst cases for the liveness-based system. \nAlso, the semantics of HiggsCheck SafeTypeScript is modified to formalize the liveness-based type system. \nOur work proves that relaxations of contractual obligations in a gradually typed system with the monotonic semantics are viable and realistic.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".