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Notice bibliographique
Résumé
The transition from an informal requirements specification in natural language to a structured, precise specification is an important challenge in practice. It is particularly so for object-oriented methods, defined in the context of the OMG's Model Driven Architecture (MDA), where a key step is to transition from a use case model to an analysis model. However, providing automated support for this transition is challenging, mostly because, in practice, requirements are expressed in natural language and are much less structured than other kinds of development artifacts. Such an automated transformation would enable at least the generation of an initial, likely incomplete, analysis model and enable automated traceability from requirements to code, through various intermediate models. In this article, we propose a method and a tool called aToucan, building on existing work, to automatically generate a UML analysis model comprising class, sequence and activity diagrams from a use case model and to automatically establish traceability links between model elements of the use case model and the generated analysis model. Note that our goal is to save effort through automated support, not to replace human abstraction and decision making. Seven (six) case studies were performed to compare class (sequence) diagrams generated by aToucan to the ones created by experts, Masters students, and trained, fourth-year undergraduate students. Results show that aToucan performs well regarding consistency (e.g., 88% class diagram consistency) and completeness (e.g., 80% class completeness) when comparing generated class diagrams with reference class diagrams created by experts and Masters students. Similarly, sequence diagrams automatically generated by aToucan are highly consistent with the ones devised by experts and are also rather complete, for instance, 91% and 97% message consistency and completeness, respectively. Further, statistical tests show that aToucan significantly outperforms fourth-year engineering students in this respect, thus demonstrating the value of automation. We also conducted two industrial case studies demonstrating the applicability of aToucan in two different industrial domains. Results showed that the vast majority of model elements generated by aToucan are correct and that therefore, in practice, such models would be good initial models to refine and augment so as to converge towards to correct and complete analysis models. A performance analysis shows that the execution time of aToucan (when generating class and sequence diagrams) is dependent on the number of simple sentences contained in the use case model and remains within a range of a few minutes. Five different software system descriptions (18 use cases altogether) were performed to evaluate the generation of activity diagrams. Results show that aToucan can generate 100% complete and correct control flow information of activity diagrams and on average 85% data flAow information completeness. Moreover, we show that aToucan outperforms three commercial tools in terms of activity diagram generation.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle