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Record W2205665593

The case for system testing with swift hierarchical VM fork

2014· article· en· W2205665593 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

VenueIEEE International Conference on Cloud Computing Technology and Science · 2014
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFork (system call)Computer scienceTest suiteCloud computingOperating systemUnit testingVirtual machineSoftware performance testingWhite-box testingSuiteEmbedded systemSoftwareTest caseSoftware systemSoftware construction
DOInot available

Abstract

fetched live from OpenAlex

System testing is an essential part of software development. Unfortunately, comprehensive testing of large systems is often resource intensive and time-consuming. In this paper, we explore the possibility of leveraging hierarchical virtual machine (VM) fork to optimize system testing in the cloud. Testing using VM fork has the potential to save system configuration effort, obviate the need to run redundant common steps, and reduce disk and memory requirements by sharing resources across test cases. A preliminary experiment that uses VM fork to run a subset of MySQL database test suite shows that the technique reduces VM run time to complete all test cases by 60%.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.049
GPT teacher head0.309
Teacher spread0.260 · 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