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Record W2034760946 · doi:10.1142/s0219877004000301

'fuzzy ProjectManager' — FRAMEWORK FOR SOFTWARE PROJECT MANAGEMENT USING FUZZY LOGIC

2004· article· en· W2034760946 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 Innovation and Technology Management · 2004
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsWestern University
Fundersnot available
KeywordsFuzzy logicComputer scienceSoftware project managementProject managementSoftwareProject planningProject management triangleSoftware engineeringSystems engineeringProcess managementData miningSoftware developmentArtificial intelligenceSoftware constructionEngineeringProgramming language

Abstract

fetched live from OpenAlex

Project Management is the application of knowledge, skills, tools and techniques to project activities in order to meet project requirements. The success of any project relies heavily on the initial estimation of all project parameters. The absence of reliable estimations leads to ineffective project planning, over- or under-commitment of resources and therefore an increased likelihood of a software project failure. Fuzzy Logic is a soft-computing technique used to effectively solve uncertainties due to imprecise inputs to generate linguistic or quantitative outputs. This paper presents a novel framework for project management incorporating fuzzy logic known as 'fuzzy ProjectManager'. Furthermore, this paper demonstrates the application of fuzzy logic as a feasible technique for improved estimation accuracy of all software project estimations to ensure higher software project success rates.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.097
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.034
GPT teacher head0.336
Teacher spread0.302 · 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