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Record W6999070018

On the Caching Schemes to Speed Up Program Reduction

2022· dissertation· en· W6999070018 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReduction (mathematics)CacheProcess (computing)DebuggingEncoding (memory)ENCODEProperty (philosophy)Key (lock)Scheme (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Program reduction is a highly practical, widely demanded technique to help debug language tools, such as compilers, interpreters and debuggers. Given a program P which exhibits a property ψ, conceptually, program reduction iteratively applies various program transformations to generate a vast number of variants from P by deleting certain tokens, and returns the minimal variant preserving ψ as the result. \n \n \nA program reduction process inevitably generates duplicate variants, and the number of them can be significant. Our study reveals that on average 62.3% of the generated variants in HDD, a state-of-the-art program reducer, are duplicates. Checking them against ψ is thus redundant and unnecessary, which wastes time and computation resources. Although it seems that simply caching the generated variants can avoid redundant property tests, such a trivial method is impractical in the real world due to the significant memory footprint. Therefore, a memory-efficient caching scheme for program reduction is in great demand. \n \n \nThis thesis is the first effort to conduct systematic, extensive analysis of memory-efficient caching schemes for program reduction. We first propose to use two well-known compression methods, i.e., ZIP and SHA, to compress the generated variants before they are stored in the cache. Furthermore, our keen understanding on the program reduction process motivates us to propose a novel, domain-specific, both memory and computation-efficient caching scheme, Refreshable Compact Caching (RCC). Our key insight is two-fold: 1) by leveraging the correlation between variants and the original program P, we losslessly encode each variant into an equivalent, compact, canonical representation; 2) we periodically remove stale cache entries to minimize the memory footprint over time. \n \n \nOur evaluation on 20 real-world C compiler bugs demonstrates that caching schemes help avoid issuing redundant queries by 62.3%; correspondingly, the runtime performance is notably boosted by 15.6%. With regard to the memory efficiency, all three methods use less memory than the state-of-the-art string-based scheme STR. ZIP and SHA cut down the memory footprint by 73.99% and 99.74%, compared to STR; more importantly, the highly-scalable, domain-specific RCC dominates peer schemes, and outperforms the second-best SHA by 89.0%.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0020.011
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.018
GPT teacher head0.233
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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