Solving Integer Constraint in Reuse Based Requirements Engineering

International audienceProduct Lines (PL) have proved an effective approach to reuse-based systems development. Several modelling languages were proposed so far to specify PL. Although they can be very different, these languages show two common features: they emphasize (a) variability, and (b) the specification of constraints to define acceptable configurations. It is now widely acknowledged that configuring a product can be considered as a constraint satisfaction problem. It is thus natural to consider constraint programming as a first choice candidate to specify constraints on PL. For instance, the different constraints that can be specified using the FODA language can easily be expressed using boolean constraints, which enables automated calculation and configuration using a SAT solver. But constraint programming proposes other domains than the boolean domain: for instance integers, real, or sets. The integer domain was, for instance, proposed by Benavides to specify constraints on feature attributes. This paper proposes to further explore the use of integer constraint programming to specify PL constraints. The approach was implemented in a prototype tool. Its use in a real case showed that constraint programming encompasses different PL modeling languages (such as FORE, OVM, or else), and allows to specify complex constraints that are difficult to specify with these languages

Using Integer Constraint Solving in Reuse Based Requirements Engineering

Product Lines (PL) have proved an effective approach to reuse-based systems development. Several modeling languages were proposed so far to specify PL. Although they can be very different, these languages show two common features: they emphasize (a) variability, and (b) the specification of constraints to define acceptable configurations. It is now widely acknowledged that configuring a product can be considered as a constraint satisfaction problem. It is thus natural to consider constraint programming as a first choice candidate to specify constraints on PL. For instance, the different constraints that can be specified using the FODA language can easily be expressed using boolean constraints, which enables automated calculation and configuration using a SAT solver. But constraint programming proposes other domains than the boolean domain: for instance integers, real, or sets. The integer domain was, for instance, proposed by Benavides to specify constraints on feature attributes. This paper proposes to further explore the use of integer constraint programming to specify PL constraints. The approach was implemented in a prototype tool. Its use in a real case showed that constraint programming encompasses different PL modeling languages (such as FORE, OVM, or else), and allows specifying complex constraints that are difficult to specify with these languages

Expressing advanced user preferences in component installation

State of the art component-based software collections - such as FOSS distributions - are made of up to dozens of thousands components, with complex inter-dependencies and conflicts. Given a particular installation of such a system, each request to alter the set of installed components has potentially (too) many satisfying answers. We present an architecture that allows to express advanced user preferences about package selection in FOSS distributions. The architecture is composed by a distribution-independent format for describing available and installed packages called CUDF (Common Upgradeability Description Format), and a foundational language called MooML to specify optimization criteria. We present the syntax and semantics of CUDF and MooML, and discuss the partial evaluation mechanism of MooML which allows to gain efficiency in package dependency solvers

Security Provisioning in Cloud Environments using Dynamic Expiration Enabled Role based Access Control Model

In cloud environment the role based access control (RBAC) system model has come up with certain promising facilities for security communities. This system has established itself as highly robust, powerful and generalized framework for providing access control for security management. There are numerous practical applications and circumstances where the users might be prohibited to consider respective roles only at certain defined time periods. Additionally, these roles can be invoked only on after pre-defined time intervals which depend on the permission of certain action or event. In order to incarcerate this kind of dynamic aspects of a role, numerous models like temporal RBAC (TRBAC) was proposed, then while this approach could not deliver anything else except the constraints of role enabling. Here in this paper, we have proposed robust and an optimum scheme called Dynamic expiration enabled role based access control (DEERBAC) model which is efficient for expressing a broad range of temporal constraints. Specifically, in this approach we permit the expressions periodically as well as at certain defined time constraints on roles, user-role assignments as well as assignment of role-permission. According to DEERBAC model, in certain time duration the roles can be further restricted as a consequence of numerous activation constraints and highest possible active duration constraints. The dominant contributions of DEERBAC model can the extension and optimization in the existing TRBAC framework and its event and triggering expressions. The predominant uniqueness of this model is that this system inherits the expression of role hierarchies and Separation of Duty (SoD) constraints that specifies the fine-grained temporal semantics. The results obtained illustrates that the DEERBAC system provides optimum solution for efficient user-creation, role assignment and security management framework in cloud environment with higher user count and the simultaneous rolepermission,

Constraints: the Heart of Domain and Application Engineering in the Product Lines Engineering Strategy

International audienceDrawing from an analogy between features based Product Line (PL) models and Constraint Programming (CP), this paper explores the use of CP in the Domain Engineering and Application Engineering activities that are put in motion in a Product Line Engineering strategy. The start idea is simple: both CP and PL engineering deal with variables, and constraints that these variables must satisfy. Therefore, specifying a PL as a constraint program instead of a feature model, or another kind of PL formalism, carries out two important qualities of CP: expressiveness and direct automation. On the one hand, variables in CP can take values over boolean, integer, real or even complex domains (i.e., lists, arrays and trees) and not only boolean values as in most PL languages such as the Feature-Oriented Domain Analysis (FODA). Specifying boolean, arithmetic, symbolic and reified constraint, provides a power of expression that spans beyond that provided by the boolean dependencies in FODA models. On the other hand, PL models expressed as constraint programs can directly be executed and analyzed by off-the-shelf solvers. Starting with a working example, this paper explores the issues of (a) how to specify a PL model using CP, including in the presence of multi-model representation, (b) how to verify PL specifications, (c) how to specify configuration requirements and (d) how to support the product configuration activity. Tests performed on a benchmark of 50 PL models show that the approach is efficient and scales up easily to very large and complex PL specification

RED-PL, a Method for Deriving Product Requirements from a Product Line Requirements Model

International audienceSoftware product lines (SPL) modeling has proven to be an effective approach to reuse in software development. Several variability approaches were developed to plan requirements reuse, but only little of them actually address the issue of deriving product requirements. Indeed, while the modeling approaches sell on requirements reuse, the associated derivation techniques actually focus on deriving and reusing technical product data.This paper presents a method that intends to support requirements derivation.Its underlying principle is to take advantage of approaches made for reuse PL requirements and to complete them by a requirements development process by reuse for single products. The proposed approach matches users' product requirements with PL requirements models and derives a collection ofrequirements that is (i) consistent, and (ii) optimal with respect to users' priorities and company's constraints. The proposed methodological process was validated in an industrial setting by considering the requirement engineering phase of a product line of blood analyzers

Value-Driven IT Project Portfolio Management: Tool-Based Scoring, Selection, and Scheduling

Managing IT project portfolios is a challenge because of IT projects\u27 complexity, dynamics, and uncertainty. Many IT projects exceed resources or time frames and do not reach their value-driven goals. A continuous scoring, selection, and scheduling of IT project proposals is thus essential to build an optimal portfolio. It has a significant impact on value contribution, strategic direction, goal achievement, and competitive advantages. We quantify an IT project\u27s urgency, strategy, efficiency, risk, and complexity as important evaluation and scoring criteria. To support top management decision makers in the IT project portfolio management process, we outline a combination of an evaluation approach with an optimization model. We develop a prototype decision support system to automate and simplify this process and demonstrate its applicability. Our recommendations address both theory and practice, improve IT project portfolio management, support value creation, and goal achievement

A metaheuristic multi-criteria optimisation approach to portfolio selection

Portfolio selection is concerned with selecting from of a universe of assets the ones in which one wishes to invest and the amount of the investment. Several criteria can be used for portfolio selection, and the resulting approaches can be classified as being either active or passive. The two approaches are thought to be mutually exclusive, but some authors have suggested combining them in a unified framework. In this work, we define a multi-criteria optimisation problem in which the two types of approaches are combined, and we introduce a hybrid metaheuristic that combines local search and quadratic programming to obtain an approximation of the Pareto set. We experimentally analyse this approach on benchmarks from two different instance classes: these classes refer to the same indexes, but they use two different return representations. Results show that this metaheuristic can be effectively used to solve multi-criteria portfolio selection problems. Furthermore, with an experiment on a set of instances coming from a different financial scenario, we show that the results obtained by our metaheuristic are robust with respect to the return representation used

The MOEADr Package – A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition

Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easier reproducibility of existing MOEA/D variants from the literature, as well as for faster development and testing of new composite algorithms. The package offers an standardized, modular implementation of MOEA/D based on this framework, which was designed aiming at providing researchers and practitioners with a standard way to discuss and express MOEA/D variants. In this paper we introduce the design principles behind the MOEADr package, as well as its current components. Three case studies are provided to illustrate the main aspects of the package