Selection of Composable Web Services Driven by User Requirements

International audienceBuilding a composite application based on Web services has become a real challenge regarding the large and diverse service space nowadays. Especially when considering the various functional and non-functional capabilities that Web services may afford and users may require. In this paper, we propose an approach for facilitating Web service selection according to user requirements. These requirements specify the needed functionality and expected QoS, as well as the composability between each pair of services. The originality of our approach is embodied in the use of Relational Concept Analysis (RCA), an extension of Formal Concept Analysis (FCA). Using RCA, we classify services by their calculated QoS levels and composability modes. We use a real case study of 901 services to show how to accomplish an efficient selection of services satisfying a specified set of functional and non-functional requirements

Towards a Formal Framework for Partial Compliance of Business Processes

Binary "YES-NO" notions of process compliance are not very helpful to managers for assessing the operational performance of their company because a large number of cases fall in the grey area of partial compliance. Hence, it is necessary to have ways to quantify partial compliance in terms of metrics and be able to classify actual cases by assigning a numeric value of compliance to them. In this paper, we formulate an evaluation framework to quantify the level of compliance of business processes across different levels of abstraction (such as task,trace and process level) and across multiple dimensions of each task (such as temporal, monetary, role-, data-, and quality-related) to provide managers more useful information about their operations and to help them improve their decision making processes. Our approach can also add social value by making social services provided by local, state and federal governments more flexible and improving the lives of citizens.Comment: 15 page, 4 figures, 2 tables; Under consideration at AICOL 2020, co-located with Juri

AOC-Poset on discourse and argumentation subgraphs: what can we learn on their dependencies?

International audienceWe aim at finding and understanding dependencies between linguistic structures which differ in terms of constraints and expressive power. It has been shown that studying dependencies between the argu-mentation structure (ARG) and the Rhetorical Structure Theory (RST) is non-trivial and requires a fine methodology. In this paper, we propose to take advantage of the AOC-Poset structure to understand how the subgraphs alignements occur in a small corpus annotated in ARG and RST. We formalize the structures as graphs from which we extract both subgraphs and subgraphs alignments, matching those subgraphs which include the same text segments. Based on these extractions, we build a formal context where the objects are the texts and the attributes are the subgraphs and the subgraphs alignments. We show what we can learn from the dependencies between the structures by mining the AOC-Poset made of these attributes

Reusable abstractions for modeling languages

This is the author’s version of a work that was accepted for publication in Information Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Systems, 38, 8, (2013) DOI: 10.1016/j.is.2013.06.001Model-driven engineering proposes the use of models to describe the relevant aspects of the system to be built and synthesize the final application from them. Models are normally described using Domain-Specific Modeling Languages (DSMLs), which provide primitives and constructs of the domain. Still, the increasing complexity of systems has raised the need for abstraction techniques able to produce simpler versions of the models while retaining some properties of interest. The problem is that developing such abstractions for each DSML from scratch is time and resource consuming. In this paper, our goal is reducing the effort to provide modeling languages with abstraction mechanisms. For this purpose, we have devised some techniques, based on generic programming and domain-specific meta-modeling, to define generic abstraction operations that can be reused over families of modeling languages sharing certain characteristics. Abstractions can make use of clustering algorithms as similarity criteria for model elements. These algorithms can be made generic as well, and customized for particular languages by means of annotation models. As a result, we have developed a catalog of reusable abstractions using the proposed techniques, together with a working implementation in the MetaDepth multi-level meta-modeling tool. Our techniques and prototypes demonstrate that it is feasible to build reusable and adaptable abstractions, so that similar abstractions need not be developed from scratch, and their integration in new or existing modeling languages is less costly.Work funded by the Spanish Ministry of Economy and Competitivity with project “Go Lite” (TIN2011-24139), and the R&D programme of Madrid Region with project “eMadrid” (S2009/TIC-1650)

Détection et correction automatique des défauts de conception au moyen de l’apprentissage automatique pour l’amélioration de la qualité des systèmes

RÉSUMÉ: La maintenance logicielle apparait comme l’activité la plus coûteuse dans le cycle du développement : plus de 80% des ressources lui sont consacrées. Au cours des activités de maintenance, l’architecture et la conception du logiciel sont très peu prises en compte. Il s’en suit une dégradation progressive de ces artefacts dus à des défauts de conception. Ces défauts peuvent avoir été introduits dès la première conception mais également par les maintenances du logiciel. La dégradation de la conception du logiciel rend encore plus difficile la compréhension du logiciel et les maintenances à venir, créant ainsi un cycle vicieux. Nous nous proposons dans ce projet de recherche de contribuer à réduire la dégradation des conceptions logicielles en mettant en place un système intégré de détection et de correction automatiques des défauts de conception et également un suivi de la qualité de la conception. Ce système, nommé SUDERCO, est basé sur l’apprentissage automatique et vise à fournir un cadre souple et évolutif pour aider à réduire les coûts de maintenance par la préservation de la conception. ---------- ABSTRACT: Software maintenance is emerging as the most expensive activity in the development cycle: more than 80% of resources are devoted to it. During maintenance activities, architecture and design of the software are rarely taken into account. It follows a progressive deterioration of these artifacts due to design defects. These defects may have been introduced not only in the first design, but also during the maintenance of the software. The degradation of software design makes it even harder to understand the software and perform future maintenance, creating a vicious cycle. We propose a research plan to contribute in minimizing the degradation of software designs by providing an integrated system for the automatic detection and correction of design defects, along with monitoring the design quality. This system, called SUDERCO, is based on machine learning techniques and aims at providing a flexible and scalable tool to help reduce maintenance costs by preserving the design