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Record W1527607785 · doi:10.1109/coginf.2003.1225966

A cognitive complexity metric based on category learning

2004· article· en· W1527607785 on OpenAlex
T. Klemola, Juergen Rilling

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 Engineering Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsProgram comprehensionComprehensionComputer scienceProcess (computing)IdentifierSet (abstract data type)SoftwareSoftware developmentMetric (unit)CognitionSoftware maintenanceArtificial intelligenceSoftware engineeringSoftware systemHuman–computer interactionProgramming languageEngineeringPsychology

Abstract

fetched live from OpenAlex

Software development is driven by software comprehension. Controlling a software development process is dependent on controlling software comprehension. Measures of factors that influence software comprehension are required in order to achieve control. The use of high-level languages results in many different kinds of lines of code that require different levels of comprehension effort. As the reader learns the set of arrangements of operators, attributes and labels particular to an application, comprehension is eased as familiar arrangements are repeated. Elements of cognition that describe the mechanics of comprehension serve as a guide to assessing comprehension demands in the understanding of programs written in high level languages. A new metric, kinds of lines of code identifier density is introduced and a case study demonstrates its application and importance. Related work is discussed.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.051
GPT teacher head0.299
Teacher spread0.248 · 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

Quick stats

Citations39
Published2004
Admission routes1
Has abstractyes

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