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Record W2547621596 · doi:10.1109/ms.2016.147

Cyclomatic Complexity

2016· article· en· W2547621596 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 Software · 2016
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsPolytechnique Montréal
FundersEngineering and Physical Sciences Research Council
KeywordsCyclomatic complexityComputer sciencePopularityMetric (unit)Software metricSoftware engineeringCode (set theory)Object (grammar)SoftwareSimple (philosophy)Software developmentProgramming languageData scienceTheoretical computer scienceSoftware qualityArtificial intelligenceEngineeringSet (abstract data type)Political scienceEpistemology

Abstract

fetched live from OpenAlex

The cyclomatic complexity (CC) metric measures the number of linearly independent paths through a piece of code. Although Thomas McCabe developed CC for procedural languages, its popularity has endured throughout the object-oriented era. That said, CC is one of the most controversial metrics, shunned for the most part by academia for certain theoretical weaknesses and the belief that it's no more useful than a simple “lines of code” metric. However, most metrics collection tools support its collection, and, paradoxically, industry uses it extensively. So, why is this the case? This question also leads to fundamental perennial questions about industry's exposure to academic opinion and whether academic research fails to take account of software development's daily practicalities. Maybe industry is simply looking for straightforward, widely understood metrics?

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.274
Teacher spread0.237 · 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