Construction et réutilisation de spécifications LOTOS

Colloque avec actes et comité de lecture. nationale.National audienceNotre objectif est d'assister le spécifieur dans sa démarche de construction, de réutilisation et d'adaptation de spécifications LOTOS. Nous utilisons pour cela le modèle Proplane qui permet de décrire différentes étapes de développement de spécification et de mémoriser les décisions prises ainsi que leurs justifications. Dans ce papier, nous présentons une approche de construction de spécifications LOTOS : le style orienté processus et étudions deux exemples de réutilisation : adjonction d'un composant dans une architecture et réutilisation d'un style d'architecture

Composition of Structured Process Specifications

AbstractThis paper provides the definition of an operator that composes two structured process specifications while preserving the original structure in the new specification. On each structuring level, this operator assembles two by two the components of the original processes, and so on until the lower level is reached where the basic components are integrated. The composition is driven by the external gates that are shared between the participating components, and components are assembled if they have the same internal structure. We associate with our operator a set of semantics conditions that ensures the correctness of the composition. The composition operator is progressively introduced with several examples

A Multi-Attribute Auction Mechanism based on Conditional Constraints and Conditional Qualitative Preferences

Auctioning multi-dimensional items is a key challenge, which requires rigorous tools. This study proposes a multi-round, first-score, semi-sealed multi-attribute reverse auction system. A fundamental concern in multi-attribute auctions is acquiring a useful description of the buyers’ individuated requirements: hard constraints and qualitative preferences. To consider real requirements, we express dependencies among attributes. Indeed, our system enables buyers eliciting conditional constraints as well as conditional preferences. However, determining the winner with diverse criteria may be very time consuming. Therefore, it is more useful for our auction to process quantitative data. A challenge here is to satisfy buyers with more facilities, and at the same time keep the auctions efficient. To meet this challenge, our system maps the qualitative preferences into a multi-criteria decision rule. It also completely automates the winner determination since it is a very difficult task for buyers to estimate quantitatively the attribute weights and define attributes value functions. Our procurement auction looks for the outcome that satisfies all the constraints and best matches the preferences. We demonstrate the feasibility and measure the time performance of the proposed system through a 10-attribute auction. Finally, we assess the user acceptance of our requirements specification and winner selection tool

Aide à la réutilisation de spécifications formelles en LOTOS

Texte intégral accessible uniquement aux membres de l'Université de LorraineReuse is a key to improve the quality and the productivity of software. However, there are still problems not entirely resolved limiting the practice of reuse such as construction of a reusable component and adaptation of a component in an application according to the specific needs of the user. In our work, we are interested to the reuse of formal specifications. These specifications allow on one hand a system description to de more abstract, explicit and modular than the code and on the other hand to prove the correction of new system. We focused on two specifications types : abstract data types and concurrent processes using LOTOS language. To support specification reuse, we defined new and complex methods : re-striction, promotion and generalization of data types as well a composition and extension of processes. These methods are aided with operators defined in Proplane model. Reuse operators are based on theories (to produce correct results), are automatic ( the users are not necessarily experts in formal methods) and interactive (to support the user intuition). We applied these operators on case studies.La réutilisation est un moyen permettant d'améliorer la qualité et la productivité des logiciels. Cependant, il existe encore des problèmes non entièrement résolus limitant la pratique de la réutilisation comme la construction d'un composant réutilisable et l'adaptation d'un composant dans une application selon les besoins spécifiques de l'utilisateur. Dans notre travail, nous nous sommes intéressés à la réutilisation de spécifications formelles. Celles-ci permettent, d'une part, une description du système de manière plus abstraite, plus explicite et plus modulaire que le code, et d'autre part, de prouver la correction du nouveau système. Nous nous sommes focalisés sur deux types de spécifications, les types abstraits de données et les processus concurrents, en utilisant le langage LOTOS. Pour supporter la réutilisation de spécifications, nous avons défini des méthodes nouvelles et élaborées : restriction, promotion et généralisation de types de données et également composition et extension de processus. Ces méthodes sont assistées à l'aide d'opérateurs définis dans le modèle Proplane. Nos opérateurs de réutilisation sont basés sur des bases formelles (afin d'engendrer des résultats corrects), sont automatiques (les utilisateurs ne sont pas forcément des experts de méthodes formelles) et inter-actifs (pour supporter l'intuition des utilisateurs). Nous avons appliqué ces opérateurs sur des études de cas

Semi-Supervised Self-Training of Hate and Offensive Speech from Social Media

Improving Offensive and Hate Speech (OHS) classifiers’ performances requires a large, confidently labeled textual training dataset. Our study devises a semi-supervised classification approach with self-training to leverage the abundant social media content and develop a robust OHS classifier. The classifier is self-trained iteratively using the most confidently predicted labels obtained from an unlabeled Twitter corpus of 5 million tweets. Hence, we produce the largest supervised Arabic OHS dataset. To this end, we first select the best classifier to conduct the semi-supervised learning by assessing multiple heterogeneous pairs of text vectorization algorithms (such as N-Grams, World2Vec Skip-Gram, AraBert and DistilBert) and machine learning algorithms (such as SVM, CNN and BiLSTM). Then, based on the best text classifier, we perform six groups of experiments to demonstrate our approach’s feasibility and efficacy based on several self-training iterations