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Record W1515229758

Maturity Assessment Framework for Business Dimension of Software Product Family

2006· article· en· W1515229758 on OpenAlexaff
Faheem Ahmed, Luiz Fernando Capretz

Bibliographic record

VenueScholarship@Western (Western University) · 2006
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsProcess managementProduct (mathematics)Software developmentComputer scienceCapability Maturity ModelSoftwareSoftware engineeringKnowledge managementBusinessMathematics
DOInot available

Abstract

fetched live from OpenAlex

The software product family approach aims at curtailing the concept of “reinventing the wheel” in the software development process. The business has been highlighted as one of the critical dimensions in the process of software product family. This work presents an assessment framework for evaluating the business dimension of software product family process. Additionally, a software product family business evaluation tool has been designed and implemented on the basis of the presented framework. The tool preprocesses the data of key business factors, and it evaluates the overall business maturity of an organization. To demonstrate the application of the framework, and to determine the current software product family business performance, we conducted a case study of an organization actively involved in the business of software product family. The framework and the tool provide direct mechanisms to evaluate the current maturity level of software product family business of an organization. This research is a contribution towards establishing a comprehensive and unified strategy for a process evaluation of the software product family.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.004
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.290
Teacher spread0.249 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2006
Admission routes1
Has abstractyes

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