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Record W138245146

Building an Experience-Base for Product-line Software Development Process

2001· article· en· W138245146 on OpenAlexaff
Mark Chandra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPraxisProcess (computing)Knowledge managementKnowledge baseProduct (mathematics)Variety (cybernetics)Computer scienceSet (abstract data type)Process managementUnit (ring theory)EngineeringWorld Wide WebPsychologyArtificial intelligencePolitical scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Abstracts to ModusPackageConcrete PackageConcretePackage Abstracts to ModusPackageConcretePackageConcrete Package Abstracts to ModusPackageConcrete PackageConcretePackage Abstracts to Figure 1: Different Levels of Experience PackagesAbstract knowledge is essential for the functioning ofthe EF Support unit. Concrete knowledge is essential forthe proper functioning of the Analysis unit. Furthermore,disseminating concrete knowledge to the project organiza-tion guides experience consumers (e.g. project manager)through the usage of the relevant abstract knowledge, espe-cially when some of the “abstracted out” details are needed.Process experiences need to be packaged in a variety ofways to fulfil different interests of its users. For example,during project planning, experience base users are more in-terested in exploring options to decide on the set of processesto use. At this stage, they are interested in process merits andmajor risks, inter-process interactions and trade-offs, ratherthan how to enact it. When a particular process is chosen,users’ interests shift to issues like comparing the differentmethodologies to enact the process, and how to measure itssuccess or manage its risks.While concrete knowledge can be packaged as one type(concrete type), abstract knowledge needs to be packageddifferently to fulfill different users’ interests. To emphasizethese differences abstract knowledge is packaged as either:praxis and modus types. The three experience package typescan be described as follows:Praxis. Praxis packages document industry best practices.Praxis packages are general in nature, documenting forexample, the efficacy of a process, the merits of a tool,with enaction details abstracted out.Modus. Modus packages focus on the details of a particularprocess or best practice. A modus package may documenta particular methodology for enacting the process and, asnecessary, clarify how to perform its sub-processes.Concrete. Concrete packages are tightly related to the realworld; they document hands-on experiences. A concretepackage reports on how an abstract package is enactedin a given organizational context, and whether the prac-tice was a success or a failure. The package may, but notnecessarily, come with a recommendation of ”what to doand/or avoid”.Generally, praxis packages capture the merits of the industrybest practices; modus packages represent methodologies ofenacting these practices and concrete packages describe theexperience gained by participation on process enactions.The three package types can be viewed as representingdevelopment experiences at different levels of abstraction,see figure 1. Each enaction of the process is acquired asa concrete package. By analyzing a set of similar concretepackages, environment particulars are abstracted out and theknowledge is represented as one modus package. Detailsof the enaction methodology are further abstracted out to bedocumented as one praxis package. For example, variousmethodologies of performing technical reviews (e.g. Fageninspection and IEEE standard review) are represented as dif-ferent modus packages. However, the merits and risks oftechnical reviews (despite the particulars of the methodol-ogy) are represented as one praxis package.

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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.318
Teacher spread0.231 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations4
Published2001
Admission routes1
Has abstractyes

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