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Record W4284973891 · doi:10.4230/lipics.ecoop.2024.13

Pure Methods for roDOT

2022· preprint· en· W4284973891 on OpenAlexfundno aff
Aleksander Boruch-Gruszecki, Ondřej Lhoták

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGrantová Agentura České Republiky
KeywordsComputer scienceSimple (philosophy)Key (lock)Programming languageType (biology)Track (disk drive)Theoretical computer scienceComputer securityEpistemology

Abstract

fetched live from OpenAlex

Object-oriented programming languages typically allow mutation of objects, but pure methods are common too. There is great interest in recognizing which methods are pure, because it eases analysis of program behavior and allows modifying the program without changing its behavior. The roDOT calculus is a formal calculus extending DOT with reference mutability. In this paper, we explore purity conditions in roDOT and pose a SEF guarantee, by which the type system guarantees that methods of certain types are side-effect free. We use the idea from ReIm to detect pure methods by argument types. Applying this idea to roDOT required just a few changes to the type system, but necessitated re-working a significant part of the soundness proof. In addition, we state a transformation guarantee, which states that in a roDOT program, calls to SEF methods can be safely reordered without changing the outcome of the program. We proved type soundness of the updated roDOT calculus, using multiple layers of typing judgments. We proved the SEF guarantee by applying the Immutability guarantee, and the transformation guarantee by applying the SEF guarantee within a framework for reasoning about safe transformations of roDOT programs. All proofs are mechanized in Coq.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.127
GPT teacher head0.256
Teacher spread0.129 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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