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Record W2994133228 · doi:10.5555/1858449.1858458

Using mock object frameworks to teach object-oriented design principles

2010· article· en· W2994133228 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

VenueJournal of computing sciences in colleges · 2010
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsObject-oriented designComputer scienceObject (grammar)Object-oriented programmingMethodClass (philosophy)Software engineeringContext (archaeology)Inheritance (genetic algorithm)Programming languageArtificial intelligence

Abstract

fetched live from OpenAlex

A well-designed reusable object-oriented software system adheres to two key object-oriented design principles -- i) program to an interface, not an implementation, and ii) favor object composition over class inheritance. Furthermore, the effect of unit testing (especially in the context of test-first or test-driven development) on quality of the resulting object-oriented software is undeniable. Teaching good object-oriented design principles in upper-level undergraduate courses in an effective way is challenging. We believe the use of mock object frameworks can help in teaching these object-oriented design principles in a pragmatic and hands-on manner. Using mock objects and mock object frameworks requires students to not only learn and understand the principles of good object-oriented design, but actively apply them in developing reusable object-oriented designs. This paper describes these broad object-oriented design principles and how to use mock object frameworks to teach object-oriented software design that is based on these principles.

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)0.007
metaresearch head score (Gemma)0.006
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.086
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.002
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 head0.050
GPT teacher head0.345
Teacher spread0.295 · 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