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Enregistrement W302099338

Visualizinq a Better Prototype: New Simulation Tools Enable More Affordable and Relevant Application Development

2005· article· en· W302099338 sur OpenAlexaboutno aff
Lauren Bielski

Notice bibliographique

RevueABA banking journal · 2005
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBusiness Process Modeling and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPolitenessBusinessPublic relationsMarketingComputer scienceManagementPolitical scienceLawEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Applications have numerous hidden costs associated with extensive reworking, and there are many development projects that fail outright, says Mitch Bishop, chief marketing officer with iRise, El Segundo, California. Much of problem comes from miscommunication during project conception. In fact, only 34% of all information technology projects are delivered on time and on budget according to Standish Group, West Yarmouth, Mass. Such waste isn't limited to private sector. The Federal Bureau of Investigation itself had to scrap a $170 million Virtual Case File project for agent desktops, as widely reported early in January, due to design flaws in application, Bishop relates. The verbal miscues during development go far beyond polite disagreements over budgets or territorial posturing. They relate to how imprecise people tend to be when attempting to translate visual, subtle, or ineffable into words. Add to that IT specialist who doesn't explain what's feasible based on what's being said and business analyst who's trying to parse specialized lingo from both parties and you've got a bad application just waiting to be hatched. It's relatively easy to describe what an application ought to do, says Carl Zetie, vice-president and analyst with Forrester Research, Cambridge, Mass. It's much harder to describe how application should function--for instance, how a trading screen should behave, he explains. Moreover, all of these business issues have been as commonplace as they are tedious and, until fairly recently, have had no easy solution. It's show me, don't tell me problem, Zetie says. Which means endless coding and recoding--just to get a prototype, never mind production model. And, at end of it all, you still might wind up with what can kindly be called, the not quite right application. Like Zetie, iRise's Bishop is in a position to know these tricks--and downfalls--of development trade. His firm has helped to launch a new genre of development tools aimed at analyst, who bridges gap between business users and IT, as opposed to developer. This is a big deal because U.S. companies, it turns out, are big believers in custom made, creating about $100 billion worth in specialized applications (versus shelfware), according to Forrester. Visualization prototyping is a rapidly expanding area attracting new firms that have slightly different approaches but all promise to help streamline customized application production. iRise, offers user interface (UI) generation capabilities; Toronto-based Sofea, provides a user modeling language (UML) approach and detailed requirements gathering capability; and Apptero, Oakland, Calif., simulates UI and business rules and also can generate web-service links to back-end systems for creating prototype environments. All offer banks new relatively inexpensive options. Addressing a common problem Many times, requirements-gathering process of is given short shrift. Poor requirements-gathering management is often a part of problem and causes project delays and additional costs, asserts Melinda Ballou, senior program director, MetaGroup, recently acquired by Gartner, Stamford, Conn. MetaGroup issued a research note on growing importance of automated requirements-gathering tools (an area, like rapid prototyping, that can streamline application development). The research concluded that their use can result in better execution of applications, which is one reason why Ballou likes Sofea's solution. We can provide user interface simulation that iRise and others provide, notes Sofea's senior vice-president of strategy Paul Smith. But we also pay careful attention to requirements-discovery-generating 'artifacts,' which are specific details about requirements written in format and language that specialists such as designers, developers, and testers understand. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,080
Score d'incertitude au seuil0,268

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,015
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0040,008
Science ouverte0,0030,004
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0800,016

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,269
Écart entre enseignants0,243 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2005
Routes d'admission1
Résumé présentoui

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