4-Acetamido­anilinium nitrate monohydrate

In the title hydrated salt, C8H11N2O+·NO3 −·H2O, the N—C bond distances [1.349 (2) and 1.413 (2) Å] along with the sum of the angles (359.88°) around the acetamide N atom clearly indicate that the heteroatom has an sp 2 character. The ammonium group is involved in a total of three N—H⋯O hydrogen bonds, two of these are with a water mol­ecule, which forms two O—H⋯O hydrogen bonds. All these hydrogen bonds link the ionic units and the water mol­ecule into infinite planar layers parallel to (100). The remaining two N—H⋯O inter­actions in which the ammoniun group is involved link these layers into an infinite three-dimensional network

Breathing Ontological Knowledge Into Feature Model Management

Feature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction or management of an FM is time-consuming and error-prone for large software projects, recent works have focused on automated operations for reverse engineering or refactoring FMs from a set of configurations/dependencies. Without prior knowledge, meaningless ontological relations (as defined by the feature hierarchy and groups) are likely to be synthesized and cause severe difficulties when reading, maintaining or exploiting the resulting FM. In this paper we define a generic, ontological-aware synthesis procedure that guides users when identifying the likely siblings or parent candidates for a given feature. We develop and evaluate a series of heuristics for clustering/weighting the logical, syntactic and semantic relationships between features. Empirical experiments on hundreds of FMs, coming from the SPLOT repository and Wikipedia, show that an hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions and offers the best support for reducing the number of features a user has to consider during the interactive selection of a hierarchy

On Product Comparison Matrices and Variability Models from a Product Comparison/Configuration Perspective

National audienceComparators and configurators have now become common in our daily activities and are usually based on Product Comparison Matrices (PCMs) to present and compare features. Based on a previous analysis of 300+ PCMs from Wikipedia, we identify the limits of existing comparators, configurators and PCMs. Variability Models (VMs) have been extensively used through the last 20 years to provide a synthetic and formal way to represent a product line. As a consequence, using VMs instead of PCMs could tackle these limits and improve comparison and configuration activities. In this paper, we present 5 research questions that focus on using VMs to represent PCMs and their applications for comparators and configurators.Les comparateurs et configurateurs de produits sont devenus des objets du quotidien et sont souvent représentés sous la forme de tableaux. L'analyse de 300+ tableaux de comparaison issus de Wikipedia a montré les limites de ceux-ci, en plus de celles des comparateurs et configurateurs. Les modèles de variabilité (MV) proposent une vue formelle et synthétique d'une ligne de produits. La formalisation de MVs à partir de matrices de comparaison permettrait d'aller au delà de ces limites et de proposer des outils de comparaison et de configuration plus avancés. Dans cet article, nous proposons 5 questions de recherche autour de l'utilisation de MVs pour la formalisation de matrices de comparaison et leur utilisation dans le cadre de comparateurs et configurateurs

Extraction automatique de modèles de variabilité

L'analyse du domaine vise à identifier et organiser les caractéristiques communes et variables dans un domaine. Dans la pratique, le coût initial et le niveau d'effort manuel associés à cette analyse constituent un obstacle important pour son adoption par de nombreuses organisations qui ne peuvent en bénéficier. La contribution générale de cette thèse consiste à adopter et exploiter des techniques de traitement automatique du langage naturel et d'exploration de données pour automatiquement extraire et modéliser les connaissances relatives à la variabilité à partir de documents informels. L'enjeu est de réduire le coût opérationnel de l’analyse du domaine. Nous étudions l'applicabilité de notre idée à travers deux études de cas pris dans deux contextes différents: (1) la rétro-ingénierie des Modèles de Features (FMs) à partir des exigences réglementaires de sûreté dans le domaine de l’industrie nucléaire civil et (2) l’extraction de Matrices de Comparaison de Produits (PCMs) à partir de descriptions informelles de produits. Dans la première étude de cas, nous adoptons des techniques basées sur l’analyse sémantique, le regroupement (clustering) des exigences et les règles d'association. L'évaluation de cette approche montre que 69% de clusters sont corrects sans aucune intervention de l'utilisateur. Les dépendances entre features montrent une capacité prédictive élevée: 95% des relations obligatoires et 60% des relations optionnelles sont identifiées, et la totalité des relations d'implication et d'exclusion sont extraites. Dans la deuxième étude de cas, notre approche repose sur la technologie d'analyse contrastive pour identifier les termes spécifiques au domaine à partir du texte, l'extraction des informations pour chaque produit, le regroupement des termes et le regroupement des informations. Notre étude empirique montre que les PCMs obtenus sont compacts et contiennent de nombreuses informations quantitatives qui permettent leur comparaison. L'expérience utilisateur montre des résultats prometteurs et que notre méthode automatique est capable d'identifier 43% de features correctes et 68% de valeurs correctes dans des descriptions totalement informelles et ce, sans aucune intervention de l'utilisateur. Nous montrons qu'il existe un potentiel pour compléter ou même raffiner les caractéristiques techniques des produits. La principale leçon à tirer de ces deux études de cas, est que l’extraction et l’exploitation de la connaissance relative à la variabilité dépendent du contexte, de la nature de la variabilité et de la nature du texte.Domain analysis is the process of analyzing a family of products to identify their common and variable features. This process is generally carried out by experts on the basis of existing informal documentation. When performed manually, this activity is both time-consuming and error-prone. In this thesis, our general contribution is to address mining and modeling variability from informal documentation. We adopt Natural Language Processing (NLP) and data mining techniques to identify features, commonalities, differences and features dependencies among related products. We investigate the applicability of this idea by instantiating it in two different contexts: (1) reverse engineering Feature Models (FMs) from regulatory requirements in nuclear domain and (2) synthesizing Product Comparison Matrices (PCMs) from informal product descriptions. In the first case study, we adopt NLP and data mining techniques based on semantic analysis, requirements clustering and association rules to assist experts when constructing feature models from these regulations. The evaluation shows that our approach is able to retrieve 69% of correct clusters without any user intervention. Moreover, features dependencies show a high predictive capacity: 95% of the mandatory relationships and 60% of optional relationships are found, and the totality of requires and exclude relationships are extracted. In the second case study, our proposed approach relies on contrastive analysis technology to mine domain specific terms from text, information extraction, terms clustering and information clustering. Overall, our empirical study shows that the resulting PCMs are compact and exhibit numerous quantitative and comparable information. The user study shows that our automatic approach retrieves 43% of correct features and 68% of correct values in one step and without any user intervention. We show that there is a potential to complement or even refine technical information of products. The main lesson learnt from the two case studies is that the exploitability and the extraction of variability knowledge depend on the context, the nature of variability and the nature of text

Extraction automatique de modèles de variabilité à partir de documents en langage naturel: Deux études de cas.

Domain analysis is the process of analyzing a family of products to identify their common and variable features. This process is generally carried out by experts on the basis of existing informal documentation. When performed manually, this activity is both time-consuming and error-prone. In this thesis, our general contribution is to address mining and modeling variability from informal documentation. We adopt Natural Language Processing (NLP) and data mining techniques to identify features, commonalities, differences and features dependencies among related products. We investigate the applicability of this idea by instantiating it in two different contexts: (1) reverse engineering Feature Models (FMs) from regulatory requirements in nuclear domain and (2) synthesizing Product Comparison Matrices (PCMs) from informal product descriptions. In the first case study, we adopt NLP and data mining techniques based on semantic analysis, requirements clustering and association rules to assist experts when constructing feature models from these regulations. In the second case study, our proposed approach relies on contrastive analysis technology to mine domain specific terms from text, information extraction, terms clustering and information clustering. The main lesson learnt from the two case studies is that the exploitability and the extraction of variability knowledge depend on the context, the nature of variability and the nature of text.L'analyse du domaine vise à identifier et organiser les caractéristiques communes et variables dans un domaine. Dans la pratique, le coût initial et le niveau d'effort manuel associés à cette analyse constituent un obstacle important pour son adoption par de nombreuses organisations qui ne peuvent en bénéficier. La contribution générale de cette thèse consiste à adopter et exploiter des techniques de traitement automatique du langage naturel et d'exploration de données pour automatiquement extraire et modéliser les connaissances relatives à la variabilité à partir de documents informels. L'enjeu est de réduire le coût opérationnel de l’analyse du domaine. Nous étudions l'applicabilité de notre idée à travers deux études de cas pris dans deux contextes différents: (1) la rétro-ingénierie des Modèles de Features (FMs) à partir des exigences réglementaires de sûreté dans le domaine de l’industrie nucléaire civil et (2) l’extraction de Matrices de Comparaison de Produits (PCMs) à partir de descriptions informelles de produits. Dans la première étude de cas, nous adoptons des techniques basées sur l’analyse sémantique, le regroupement des exigences et les règles d'association. Dans la deuxième étude de cas, notre approche repose sur la technologie d'analyse contrastive pour identifier les termes spécifiques au domaine à partir du texte, l'extraction des informations pour chaque produit, le regroupement des termes et le regroupement des informations. La principale leçon à tirer de ces deux études de cas, est que l’extraction et l’exploitation de la connaissance relative à la variabilité dépendent du contexte, de la nature de la variabilité et de la nature du texte

An Ontologic-Aware Feature Modeling Environment

National audienceFeature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. The manual construction or management of an FM has proven to be time-consuming and error-prone. In this paper, we introduce a feature modeling environment, built on top of the FAMILIAR language, that assists users in synthesizing FMs. Important automated operations (reverse engineering, refactoring, diff, merging, slicing) are now equipped with ontological capabilities while they guarantee the synthesis of FMs conformant to a given set of constraints. Users can interactively choose a hierarchy through ranking lists and clusters that are automatically computed by different heuristics. The tooling support opens avenues for reverse engineering and maintaining highly configurable systems.Les modèles de features (MFs) sont un formalisme populaire pour la modélisation et l'analyse des configurations des lignes de produits logiciels. L'élaboration et la gestion manuelle d'un MF prennent du temps, sont sujets à erreur et ne sont pas réalistes pour de grands projets logiciels. Dans ce papier, nous introduisons un environnement de modélisation de MFs, basé sur le langage FAMILIAR, qui assiste les utilisateurs dans la synthèse des MFs. Cet environnement enrichit de connaissances ontologiques d'importantes opérations automatiques (rétro-ingénierie, refactoring, différence, fusion, découpage) tout en garantissant la synthèse d'un MF conforme à l'ensemble de contraintes défini en entrée. Les utilisateurs peuvent choisir une hiérarchie d'une manière interactive à travers des listes triées de parents candidats et des groupes de features calculés par différentes heuristiques. Notre outil ouvre de nombreuses perspectives pour la rétro-ingénierie et la maintenance des systèmes hautement configurables

Breathing Ontological Knowledge Into Feature Model Management

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On Product Comparison Matrices and Variability Models from a Product Comparison/Configuration Perspective

National audienceComparators and configurators have now become common in our daily activities and are usually based on Product Comparison Matrices (PCMs) to present and compare features. Based on a previous analysis of 300+ PCMs from Wikipedia, we identify the limits of existing comparators, configurators and PCMs. Variability Models (VMs) have been extensively used through the last 20 years to provide a synthetic and formal way to represent a product line. As a consequence, using VMs instead of PCMs could tackle these limits and improve comparison and configuration activities. In this paper, we present 5 research questions that focus on using VMs to represent PCMs and their applications for comparators and configurators.Les comparateurs et configurateurs de produits sont devenus des objets du quotidien et sont souvent représentés sous la forme de tableaux. L'analyse de 300+ tableaux de comparaison issus de Wikipedia a montré les limites de ceux-ci, en plus de celles des comparateurs et configurateurs. Les modèles de variabilité (MV) proposent une vue formelle et synthétique d'une ligne de produits. La formalisation de MVs à partir de matrices de comparaison permettrait d'aller au delà de ces limites et de proposer des outils de comparaison et de configuration plus avancés. Dans cet article, nous proposons 5 questions de recherche autour de l'utilisation de MVs pour la formalisation de matrices de comparaison et leur utilisation dans le cadre de comparateurs et configurateurs

Breathing Ontological Knowledge Into Feature Model Synthesis: An Empirical Study

International audienceFeature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction of an FM is time-consuming and error-prone, management operations have been developed for reverse engineering, merging, slicing, or refactoring FMs from a set of configurations/dependencies. Yet the synthesis of meaningless ontological relations in the FM – as defined by its feature hierarchy and feature groups – may arise and cause severe difficulties when reading, maintaining or exploiting it. Numerous synthesis techniques and tools have been proposed, but only a few consider both configuration and ontolog-ical semantics of an FM. There are also few empirical studies investigating ontological aspects when synthesizing FMs. In this article, we define a generic, ontologic-aware synthesis procedure that computes the likely siblings or parent candidates for a given feature. We develop six heuristics for clustering and weighting the logical, syntactical and semantical relationships between feature names. We then perform an empirical evaluation on hundreds of FMs, coming from the SPLOT repository and Wikipedia. We provide evidence that a fully automated synthesis (i.e., without any user intervention) is likely to produce FMs far from the ground truths. As the role of the user is crucial, we empirically analyze the strengths and weak-nesses of heuristics for computing ranking lists and different kinds of clusters. We show that a hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions. We believe our approach, environment, and empirical results support researchers and practitioners working on reverse engineering and management of FMs

Moving Toward Product Line Engineering in a Nuclear Industry Consortium

International audienceNuclear power plants are some of the most sophisticated and complex energy systems ever designed. These systems perform safety critical functions and must conform to national safety institutions and international regulations. In many cases, regulatory documents provide very high level and ambiguous requirements that leave a large margin for interpretation. As the French nuclear industry is now seeking to spread its activities outside France, it is but necessary to master the ins and the outs of the variability between countries safety culture and regulations. This sets both an industrial and a scientific challenge to introduce and propose a product line engineering approach to an unaware industry whose safety culture is made of interpretations, specificities, and exceptions. This paper presents our current work within the French R&D project CONNEXION, while introducing variability modeling to the French nuclear industry. In particular, we discuss the background, the quest for the best variability paradigm, the practical modeling of requirements variability as well as the mapping between variable requirements and variable architecture elements