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Purpose In the present article, the authors tackle the problem of IT documentation, which plays an important role in information technology (IT) project management. Design/methodology/approach They provide a simple tool based on five complementary views, which should be detailed by the project team using a classic source code management platform. Findings The proposed tool is open source and may be reused by any IT team in various project contexts and heterogeneous development methods. Originality/value This research provides an operational framework, which facilitates IT project management and documentation. The framework is open source and may be easily downloaded by any other IT team.

The tax code, as a legislative text, defines a mathematical function that computes the income tax of a fiscal household. In order to collect taxes, this function is implemented as an algorithm by the Direction Générale des Finances Publiques (DGFiP), using a domain specific language called M (standing for "Macro-language"). We propose a formal semantics of the M language, tested thanks to data published by the DGFiP. This formalization, coupled with the public release by the DGFiP of the codebase used to compute the income tax, allowed us to produce a fully formalized artifact encoding the fragment of the tax code describing the income tax computation. We demonstrate the usefulness of such a formalization thanks to a prototype that uses an SMT solver to infer meta-properties on the income tax computation. These meta-properties could complete and refine the existing economical analysis of the redistributive effects of the income tax, as well as various social benefits. More generally, a systematic formalization of the algorithmic fragments of the law would raise the assurance level on the coherence of the French socio-fiscal system. This work has led to the development of three software artifacts: a mechanized semantics for M, an interpreter and compiler for M based on this semantics (written in OCaml), and a Python prototype of encoding the income tax computation into the Z3 SMT solver., Le code des impôts définit dans son texte législatif une fonction mathématique per-mettant de calculer l'impôt sur le revenu d'un foyer fiscal. Afin de recouvrer l'impôt, cette fonction est implémentée sous la forme d'un algorithme par la Direction Générale des Finances Publiques (DGFiP), en utilisant un langage dédié appelé M (pour macro-langage). Nous proposons une sémantique formelle du langage M, testée grâce aux données publiées par la DGFiP. Cette formalisation, couplée à la publication par la DGFiP de la base de code M calculant l'impôt, nous donne accès à une formalisation complète de la portion du code des impôts définissant l'algorithme de calcul de l'impôt sur le revenu. Nous démontrons l'utilité d'une telle formalisation grâce à un prototype à base de solveurs SMT permet-tant d'inférer des méta-propriétés sur le calcul de l'impôt. Ces méta-propriétés peuvent ensuite compléter et affiner les analyses économiques existantes sur les effets redistributifs de l'impôt sur le revenu, mais aussi de diverses allocations. Plus généralement, une for-malisation systématique des portions algorithmiques de la loi permettrait d'augmenter le niveau d'assurance sur la cohérence du système socio-fiscal français. Trois artefacts logiciels accompagnent cet article : une formalisation mécanisée de la sémantique du langage M, un compilateur pour le langage M basé sur cette sémantique, ainsi que le prototype d'encodage du code des impôts dans le solveur SMT Z3.

This paper deals with the problem of reducing technical debt and modernising legacy applications, particularly COBOL heritage on 'mainframe' computers (a.k.a. 'mainframes'). The proposed approach aims at offering a solution, which is efficient through a strong relation with a cost-effective method about transformation. Indeed, literature shows both technical and scientific solutions whose economic facet is often totally ignored, letting us understand that redoing all from scratch might be cheaper, but proofs about this are urgently expected, an often missing link. Indeed, it is essential to remind that the cost of transformation is a critical concern, especially when the to-be-transformed applications rely on millions of lines of, very often, odd COBOL.

The purpose of this paper is to describe the interest of opening legacy systems in large organisations instead of replacing them from scratch. The author also provides a feedback on the different REST solutions available to facilitate their usage by information technology architects. The paper underlines the importance not only to preserve the good functioning of the legacy heritage but also to migrate progressively applications to modernised languages. Assuredly, the code developed is robust and tackles the whole business perimeter whereas new technologies may suffer from a lack of stability or technical expertise within the organisation. This advocates for a progressive migration in the specific context of essential public services. To achieve the migration, the author proposes a hybrid architecture which relies on data exchanges between the legacy and modern parts of the application. Finally, a method is proposed to select an API management solution adjusted to each organisation requirements.

In this paper we develop a discrete and distributed optimization framework that maximizes the overall spectral efficiency of heterogeneous networks while ensuring a minimum fairness among users. We split the problem into two subproblems: the first one is handled by the users and consists in a transmission by each user of a minimum rate requirement to the base stations and the second is the resource allocation performed by the base stations with respect to the rate constraints generated by the users. The first subproblem is solved using a process where each user tries to adjust its rate requirements to the network capacity. This process is inspired from the TCP Vegas algorithm [1], where the network usage is improved by reducing congestion. Based on the users' rate requirements, the base stations perform a discrete resource optimization in order to allocate power and rate to each user. The rates are selected among a finite set of discrete rates since Adaptive Modulation and Coding (AMC) technique is assumed to be used in each network. The proposed algorithm is compared to an extension of the Proportional Fair algorithm, developed in this paper to allow joint rate and power allocation. Simulation results show that the proposed algorithm provides better performance than the simple PF while ensuring an acceptable fairness among the users.

In this thesis, we showed at first the need to develop radio resource allocation in heterogeneous mobile systems. The research results presented here aim to find methods enabling to perform this resource allocation. The selected approach consists in describing mathematically the resource allocation problem and then solve it in a distributed manner, using discrete and convex optimization.The convex optimization enables to reduce significantly computational costs, thanks to the use of convex optimization properties. Indeed, when using convex functions, any local minimum is also a global one. Moreover, the dual problem can be easily decomposed which enables the use of distributed solutions.We chose to model mobile technologies with discrete transmission modes between transceivers and receivers. This hypothesis relies on the fact that many existing radio technologies exchange information using a limited number of transmission modes. This hypothesis enables to reduce the number of possible solutions and consequently improves the solution proposed.Finally, we proved the efficiency of distributed approaches to solve the radio resource allocation problem within heterogeneous systems. Indeed, these methods are reactive to any change within radio conditions (transmission channel value, mobile movement, etc.) and are more efficient interms of computational cost as the computations are divided between the base stations and the mobiles.; Dans celte thèse, nous avons mis en évidence la nécessité de développer des approches permettant d'assurer l'allocation de ressources dans un système hétérogène. Les recherches effectuées visent à pallier l'absence d'approche générique dans le domaine de l'allocation de ressources dans des réseaux hétérogènes. L'approche retenue consiste à décrire le problème de manière mathématique puis à le résoudre de manière distribuée à l'aide de l'optimisation discrète et convexe. L'optimisation convexe permet de réduire amplement la complexité calculatoire par l'utilisation des proprietés d'une fonction convexe, à savoir le fait qu'un minimum local est également minimum global, que le problème dual peut aisément être décomposé permettant ainsi l'utilisation de méthodes distribuées. La modélisation des technologies de télécommunication s'effectue à l'aide de modes de transmission discrets entre les mobiles et les stations de base. Celte hypothèse repose sur la constatation de l'utilisation, dans les technologies de communication existantes, de schémas de transmission prédéfinis et disponibles en nombre limité. Elle permet de limiter l'espace des solutions possibles et améliore ainsi l'efficacité de la solution proposée. Enfin, nous avons montré l'intérêt d'utiliser des méthodes distribuées dans le cadre l'allocation de ressources dans des systèmes variés. En effet, ces méthodes se révèlent à la fois plus réactives aux variations des conditions du scénario (valeurs des canaux de transmission, déplacement des mobiles,. . . ) et moins coûteuses en terme de calcul car celui-ci est réparti parmi les différentes entités du système.

The objective of this paper is two fold: proposes a distributed resource optimization framework in the context of heterogeneous systems and optimizes allocation of discrete resources and bit rates to users within each system (network). In fact, most of existing wireless techniques use Adaptive Modulation and Coding technique where a finite set of Modulation and Coding schemes is available in the system. The bit rates to attribute to users are then selected among a finite set of available discrete rates. More specifically, the objective in this paper is to minimize to overall transmit power of the whole system while ensuring to each user a minimum data rate and fulfilling a maximum power constraint within each system. The use of centralized optimization frameworks to handle Radio Resource Management (RRM) problem in heterogeneous systems requires huge signaling overhead. In order to obtain distributed resource allocation, we approach the problem by using a convex optimization framework and thus split it into different subproblems each one performed by a given agent of the network(base station or user). We propose then a distributed algorithm based on successive iterations (negotiations) between the base stations and users. In each iteration, each network node (user or base station) has to handle a discrete optimization problem using an efficient (low complexity) algorithm described in the paper. By using in each iteration an appropriate update method, based on the subgradient of the dual Lagrangian objective function, our approach converges to the optimal solution with zero duality gap and after a finite number of iterations. Simulation results show good performance of the proposed algorithm and the convergence to the global optimal solution.

Nowadays, the emergence of many radio technologies has increased the research interest towards Radio Resources Management (RRM) in heterogeneous systems. In this context, we seek a distributed scheme for discrete resource allocation. The algorithm proposed should reduce computation and signalling overhead, compared to centralized optimization or distributed solutions based on Game Theory. Therefore, the solution proposed here consists in splitting the resource allocation problem into two parts. First, Users and Base Stations negotiate a mean allocation using a Multisystem Minimum Mean Rate Scheduling (M3RS) algorithm presented here. Then, Base Stations allocate power independantly and instantaneously in order to cope with changing radio conditions. This problem is widely known in literature and is not developped here.

This paper deals with aircraft recognition from an airborne platform. The data available are high-resolution radar range profiles. The classification method is probabilistic and supervised. It consists in modelling targets by scattering centers and a further association of peaks from the measure to these scattering centers. The global nearest neighbour algorithm performs the association.