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Record W2049790740 · doi:10.4018/jssci.2009040101

Exploring the Cognitive Foundations of Software Engineering

2009· article· en· W2049790740 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

VenueInternational Journal of Software Science and Computational Intelligence · 2009
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceSocial software engineeringSoftware engineeringSoftware requirementsEngineering informaticsInformaticsSoftware developmentSoftware constructionSoftwareData scienceProgramming languageHealth informaticsEngineering

Abstract

fetched live from OpenAlex

It is recognized that software is a unique abstract artifact that does not obey any known physical laws. For software engineering to become a matured engineering discipline like others, it must establish its own theoretical framework and laws, which are perceived to be mainly relied on cognitive informatics and denotational mathematics, supplementing to computing science, information science, and formal linguistics. This paper analyzes the basic properties of software and seeks the cognitive informatics foundations of software engineering. The nature of software is characterized by its informatics, behavioral, mathematical, and cognitive properties. The cognitive informatics foundations of software engineering are explored on the basis of the informatics laws of software and software engineering psychology. A set of fundamental cognitive constraints of software engineering, such as intangibility, complexity, indeterminacy, diversity, polymorphism, inexpressiveness, inexplicit embodiment, and unquantifiable quality measures, is identified. The conservative productivity of software is revealed based on the constraints of human cognitive capacity.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.063
GPT teacher head0.309
Teacher spread0.246 · 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