Modeling and Reasoning about Software Systems Containing Uncertainty and Variability
Notice bibliographique
Résumé
Summary form only given. When building large software-intensive systems, engineers need to express and reason about at least two different types of choices. One type concerns uncertainty - choosing between different design alternatives, resolving inconsistencies, or resolving conflicting stakeholder requirements. Another type deals with variability - supporting different variants of software that serve multiple customers or market segments. Partial modeling has been proposed as a technique for managing uncertainty within a software model. A partial model explicates points of uncertainty and represents the set of possible models that could be obtained by making decisions and resolving the uncertainty. Methods for reasoning about the entire set of possibilities, transforming the entire set and uncertainty-reducing refinements have recently been developed. Software product line engineering approaches propose techniques for managing the variability within sets of related software product variants. Such approaches explicate points of variability (a.k.a.features) and relationships between them in an artifact usually referred to as a feature model. A selection of features from this model guides the derivation of a specific product of a software product line (SPL). Techniques for reasoning about sets of SPL products, transforming the entire SPL and supporting their partial configuration have recently been developed. Partial models and SPL representations are naturally quite similar - both provide ways of encoding and managing sets of artifacts. The techniques for representing, reasoning with and manipulating these sets, naturally, have much in common. Yet, the goals for creating these product sets are quite different, and thus the two techniques lead to distinct methodological considerations. Uncertainty is an aspect of the development process itself; it is transient and must be reduced and eventually eliminated as knowledge is gathered and decisions are made. Thus, the ultimate goal of resolving uncertainty is to produce only one desired artifact. On the other hand, variability is an aspect of the artifacts simultaneously managed through the entire development process; it is to be preserved and carefully engineered to represent the desired range of product variants required. Thus, product lines aim to produce and simultaneously manage multiple artifacts. In this talk, I will survey approaches to representing, reasoning with and transforming models with uncertainty and variability, separately, as well as discuss current work on trying to combine the two approaches.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».