A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation

Communication networks form the backbone of our society. Topology control algorithms optimize the topology of such communication networks. Due to the importance of communication networks, a topology control algorithm should guarantee certain required consistency properties (e.g., connectivity of the topology), while achieving desired optimization properties (e.g., a bounded number of neighbors). Real-world topologies are dynamic (e.g., because nodes join, leave, or move within the network), which requires topology control algorithms to operate in an incremental way, i.e., based on the recently introduced modifications of a topology. Visual programming and specification languages are a proven means for specifying the structure as well as consistency and optimization properties of topologies. In this paper, we present a novel methodology, based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints. More specifically, we model the possible modifications of a topology control algorithm and the environment using graph transformation rules, and we describe consistency and optimization properties using graph constraints. On this basis, we apply and extend a well-known constructive approach to derive refined graph transformation rules that preserve these graph constraints. We apply our methodology to re-engineer an established topology control algorithm, kTC, and evaluate it in a network simulation study to show the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the referenced journal articl

A tractable class of binary VCSPs via M-convex intersection

A binary VCSP is a general framework for the minimization problem of a function represented as the sum of unary and binary cost functions. An important line of VCSP research is to investigate what functions can be solved in polynomial time. Cooper and \v{Z}ivn\'{y} classified the tractability of binary VCSP instances according to the concept of "triangle," and showed that the only interesting tractable case is the one induced by the joint winner property (JWP). Recently, Iwamasa, Murota, and \v{Z}ivn\'{y} made a link between VCSP and discrete convex analysis, showing that a function satisfying the JWP can be transformed into a function represented as the sum of two quadratic M-convex functions, which can be minimized in polynomial time via an M-convex intersection algorithm if the value oracle of each M-convex function is given. In this paper, we give an algorithmic answer to a natural question: What binary finite-valued CSP instances can be represented as the sum of two quadratic M-convex functions and can be solved in polynomial time via an M-convex intersection algorithm? We solve this problem by devising a polynomial-time algorithm for obtaining a concrete form of the representation in the representable case. Our result presents a larger tractable class of binary finite-valued CSPs, which properly contains the JWP class.Comment: Full version of a STACS'18 pape

Transformation Techniques for OCL Constraints

Constraints play a key role in the definition of conceptual schemas. In the UML, constraints are usually specified by means of invariants written in the OCL. However, due to the high expressiveness of the OCL, the designer has different syntactic alternatives to express each constraint. The techniques presented in this paper assist the designer during the definition of the constraints by means of generating equivalent alternatives for the initially defined ones. Moreover, in the context of the MDA, transformations between these different alternatives are required as part of the PIM-to-PIM, PIM-to-PSM or PIM-to-code transformations of the original conceptual schema

UML Profile for Mining Process: Supporting Modeling and Simulation Based on Metamodels of Activity Diagram

An UML profile describes lightweight extension mechanism to the UML by defining custom stereotypes, tagged values, and constraints. They are used to adapt UML metamodel to different platforms and domains. In this paper we present an UML profile for models supporting event driving simulation. In particular, we use the Arena simulation tool and we focus on the mining process domain. Profiles provide an easy way to obtain well-defined specifications, regulated by the Object Management Group (OMG). They can be used as a presimulation technique to obtain solid models for the mining industry. In this work we present a new profile to extend the UML metamodel; in particular we focus on the activity diagram. This extended model is applied to an industry problem involving loading and transportation of minerals in the field of mining process.Fil: Giubergia, Andrea. Universidad Nacional de San Luis. Facultad de Ciencias Fisico- Matematicas y Naturales; ArgentinaFil: Riesco, Daniel. Universidad Nacional de San Luis. Facultad de Ciencias Fisico Matematicas y Naturales. Departamento de Informatica; ArgentinaFil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Printista, Alicia Marcela. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; Argentin

Rapid adaptation in online continual learning: are we evaluating it right?

We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL) algorithms through the metric of online accuracy, which measures the accuracy of the model on the immediate next few samples. However, we show that this metric is unreliable, as even vacuous blind classifiers, which do not use input images for prediction, can achieve unrealistically high online accuracy by exploiting spurious label correlations in the data stream. Our study reveals that existing OCL algorithms can also achieve high online accuracy, but perform poorly in retaining useful information, suggesting that they unintentionally learn spurious label correlations. To address this issue, we propose a novel metric for measuring adaptation based on the accuracy on the near-future samples, where spurious correlations are removed. We benchmark existing OCL approaches using our proposed metric on large-scale datasets under various computational budgets and find that better generalization can be achieved by retaining and reusing past seen information. We believe that our proposed metric can aid in the development of truly adaptive OCL methods. We provide code to reproduce our results at https://github.com/drimpossible/EvalOCL

Deriving OCL Optimization Patterns from Benchmarks

Writing queries and navigation expressions in OCL is an important part of the task of developing a model transformation definition. When such queries are complex and the size of the models is significant, performance issues cannot be neglected. In this paper we present five patterns intended to optimize the performance of model transformations when OCL queries are involved. For each pattern we will give an example as well as several implementation alternatives. Experimental data gathered by running benchmarks is also shown to compare the alternatives. The model transformation benchmark framework developed to obtain these results is also described

Conflict Detection for Edits on Extended Feature Models using Symbolic Graph Transformation

Feature models are used to specify variability of user-configurable systems as appearing, e.g., in software product lines. Software product lines are supposed to be long-living and, therefore, have to continuously evolve over time to meet ever-changing requirements. Evolution imposes changes to feature models in terms of edit operations. Ensuring consistency of concurrent edits requires appropriate conflict detection techniques. However, recent approaches fail to handle crucial subtleties of extended feature models, namely constraints mixing feature-tree patterns with first-order logic formulas over non-Boolean feature attributes with potentially infinite value domains. In this paper, we propose a novel conflict detection approach based on symbolic graph transformation to facilitate concurrent edits on extended feature models. We describe extended feature models formally with symbolic graphs and edit operations with symbolic graph transformation rules combining graph patterns with first-order logic formulas. The approach is implemented by combining eMoflon with an SMT solver, and evaluated with respect to applicability.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

From examples to knowledge in model-driven engineering : a holistic and pragmatic approach

Le Model-Driven Engineering (MDE) est une approche de développement logiciel qui propose d’élever le niveau d’abstraction des langages afin de déplacer l’effort de conception et de compréhension depuis le point de vue des programmeurs vers celui des décideurs du logiciel. Cependant, la manipulation de ces représentations abstraites, ou modèles, est devenue tellement complexe que les moyens traditionnels ne suffisent plus à automatiser les différentes tâches. De son côté, le Search-Based Software Engineering (SBSE) propose de reformuler l’automatisation des tâches du MDE comme des problèmes d’optimisation. Une fois reformulé, la résolution du problème sera effectuée par des algorithmes métaheuristiques. Face à la pléthore d’études sur le sujet, le pouvoir d’automatisation du SBSE n’est plus à démontrer. C’est en s’appuyant sur ce constat que la communauté du Example-Based MDE (EBMDE) a commencé à utiliser des exemples d’application pour alimenter la reformulation SBSE du problème d’apprentissage de tâche MDE. Dans ce contexte, la concordance de la sortie des solutions avec les exemples devient un baromètre efficace pour évaluer l’aptitude d’une solution à résoudre une tâche. Cette mesure a prouvé être un objectif sémantique de choix pour guider la recherche métaheuristique de solutions. Cependant, s’il est communément admis que la représentativité des exemples a un impact sur la généralisabilité des solutions, l'étude de cet impact souffre d’un manque de considération flagrant. Dans cette thèse, nous proposons une formulation globale du processus d'apprentissage dans un contexte MDE incluant une méthodologie complète pour caractériser et évaluer la relation qui existe entre la généralisabilité des solutions et deux propriétés importantes des exemples, leur taille et leur couverture. Nous effectuons l’analyse empirique de ces deux propriétés et nous proposons un plan détaillé pour une analyse plus approfondie du concept de représentativité, ou d’autres représentativités.Model-Driven Engineering (MDE) is a software development approach that proposes to raise the level of abstraction of languages in order to shift the design and understanding effort from a programmer point of view to the one of decision makers. However, the manipulation of these abstract representations, or models, has become so complex that traditional techniques are not enough to automate its inherent tasks. For its part, the Search-Based Software Engineering (SBSE) proposes to reformulate the automation of MDE tasks as optimization problems. Once reformulated, the problem will be solved by metaheuristic algorithms. With a plethora of studies on the subject, the power of automation of SBSE has been well established. Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions. However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions. We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities

Data-Flow Based Model Analysis

The concept of (meta) modeling combines an intuitive way of formalizing the structure of an application domain with a high expressiveness that makes it suitable for a wide variety of use cases and has therefore become an integral part of many areas in computer science. While the definition of modeling languages through the use of meta models, e.g. in Unified Modeling Language (UML), is a well-understood process, their validation and the extraction of behavioral information is still a challenge. In this paper we present a novel approach for dynamic model analysis along with several fields of application. Examining the propagation of information along the edges and nodes of the model graph allows to extend and simplify the definition of semantic constraints in comparison to the capabilities offered by e.g. the Object Constraint Language. Performing a flow-based analysis also enables the simulation of dynamic behavior, thus providing an "abstract interpretation"-like analysis method for the modeling domain