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Record W4387793311 · doi:10.1145/3618305.3623601

An Optimal Structure-Aware Code Difference Framework with MaxSAT-Solver

2023· article· en· W4387793311 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaximum satisfiability problemComputer scienceSolverCode (set theory)Parallel computingAlgorithmComputational scienceProgramming languageBoolean function

Abstract

fetched live from OpenAlex

The Abstract Syntax Tree (AST) serves as a pivotal representation of program codes, offering a structured and hierarchical view of the program’s syntax. When developers modify code, the underlying AST also evolves to reflect these changes. Tree-diff algorithms, such as truediff and Gumtreediff, are developed to compare different versions of the AST and identify the modifications made between them. However, these heuristics are based on certain vertex matching methods that do not ensure optimality and preciseness. In this study, I propose a novel tree-diff approach that utilizes a MaxSAT (Maximum satisfiability) solver to address this issue. By encoding potential vertex matches and edges with associated costs as a tree-diff SAT problem, the MaxSAT solver effectively minimizes the edit distance and reveals the optimal vertex matching plan.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.455
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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