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Record W2975553208 · doi:10.22215/etd/2015-11161

Mapping ACL/VF to a Java Environment: A Feasibility Study

2015· dissertation· en· W2975553208 on OpenAlexaff
Howard Needham

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsJavaComputer scienceUSableContext (archaeology)Software engineeringFocus (optics)Programming languageOperating systemWorld Wide Web

Abstract

fetched live from OpenAlex

There are a number of different approaches to runtime validation, and one such method is Model Based Testing (MBT), a quality assurance technique where a test suite is generated from an abstract model. While there exists a number of different approaches to accomplish modelbased testing, most are state-based (and thus faced with problems such as state explosion, correspondence to code, etc.). In this thesis, we instead focus on an alternative approach to MBT, namely scenario based testing, which we consider in the context of runtime validation. More specifically, we address scenario monitoring and validation in ACL/VF ACL/VF. This approach, developed by Dr. Corriveau and his research team, provides both a language (ACL -Another Contract Language) to specify an implementation-independent testable model and a tool (VF -the Validation Framework) to validate an implementation against an ACL model.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.099
GPT teacher head0.357
Teacher spread0.258 · 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 designOther design
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

Citations0
Published2015
Admission routes1
Has abstractyes

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