MétaCan
Menu
Back to cohort
Record W2163844940 · doi:10.1109/ccece.2011.6030533

Integration testing object-oriented software systems: An experiment-driven research approach

2011· article· en· W2163844940 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 Testing and Debugging Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsIntegration testingUnit testingComputer scienceClass (philosophy)Process (computing)White-box testingSystem integrationSoftware engineeringSystem integration testingTest strategySoftwareSystems engineeringProgramming languageArtificial intelligenceSoftware constructionSoftware developmentEngineeringDatabase

Abstract

fetched live from OpenAlex

Any incremental class testing approach has to answer the two following questions: What integration process, indicating in which order classes are (integration) tested, should be selected? Which test design techniques should be applied to unit and integration test classes? Although there is a fairly large number of reported works on both questions, much remains to be done. On the one hand, class unit/integration testing techniques have mostly been described without any consideration for any integration process. On the other hand, integration processes have been suggested without much consideration for how they could be used in practice, along with class unit/integration testing techniques. There is a lack of research and practical results on how a class integration process can be used to conduct class unit/integration testing. This paper summarizes the problem and suggests an experiment-driven research approach to solve it.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.961
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.249
GPT teacher head0.357
Teacher spread0.107 · 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