Raúl Mazo

Dynamic adaptation of service compositions with variability models

Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.

Using Constraint Programming to Manage Configurations in Self-Adaptive Systems

Combining goal-modeling techniques with constraint programming provides the means to identify the variants best suited to the environmental contexts that a self-adaptive software system might encounter at runtime.

Constraints: The core of product line engineering

Product line engineering is a reuse-driven development paradigm based on the management of variability, which was successfully applied in information systems engineering and other domains. A common way to represent variability is with variability models that describe artefacts, and the dependencies between their various inflexions. Constraint programming, and in particular Boolean constraint programming, has been used so far to support analysis of variability models such as Feature-Oriented Domain Analysis (FODA) and the like. This paper goes a step further by using constraint programming to specify product lines. The focus on variability, variation points or dependencies is switched to the concept of constraints that apply to variables. The paper shows that this approach is richer than the one based on dependencies. For instance, many constraints that were needed in the cases we explored cannot be specified with dependencies of existing product line modelling languages. The approach was implemented in a prototype tool, and its scalability explored with industry case studies. These experiments show that constraint programming encompasses existing product line modelling languages such as FODA or OVM (Orthogonal Variability Model) and opens way to new possibilities such as reasoning simultaneously with different models during domain or application engineering.

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.

An Ontological Rule-Based Approach for Analyzing Dead and False Optional Features in Feature Models

Feature models are a common way to represent variability requirements of software product lines by expressing the set of feature combinations that software products can have. Assuring quality of feature models is thus of paramount importance for assuring quality in software product line engineering. However, feature models can have several types of defects that disminish benefits of software product line engineering.Two of such defects are dead features and false optional features. Several state-of-the-art techniques identify these defects, but only few of them tackle the problem of identifying their causes. Besides, the explanations they provide are cumbersome and hard to understand by humans. In this paper, we propose an ontological rule-based approach to: (a) identify dead and false optional features; (b)identify certain causes of these defects; and (c) explain these causes in natural language helping modelers to correct found defects. We represent our approach with a feature model taken from literature. A preliminary empirical evaluation of our approach over 31 FMs shows that our proposal is effective, accurate and scalable to 150 features.

A Security Ontology for Security Requirements Elicitation

Security is an important issue that needs to be taken into account at all stages of information system development, including early requirements elicitation. Early analysis of security makes it possible to predict threats and their impacts and define adequate security requirements before the system is in place. Security requirements are difficult to elicit, analyze, and manage. The fact that analysts’ knowledge about security is often tacit makes the task of security requirements elicitation even harder. Ontologies are known for being a good way to formalize knowledge. Ontologies, in particular, have been proved useful to support reusability. Requirements engineering based on predefined ontologies can make the job of requirement engineering much easier and faster. However, this very much depends on the quality of the ontology that is used. Some security ontologies for security requirements have been proposed in the literature. None of them stands out as complete. This paper presents a core and generic security ontology for security requirements engineering. Its core and generic status is attained thanks to its coverage of wide and high-level security concepts and relationships. We implemented the ontology and developed an interactive environment to facilitate the use of the ontology during the security requirements engineering process. The proposed security ontology was evaluated by checking its validity and completeness compared to other ontologies. Moreover, a controlled experiment with end-users was performed to evaluate its usability.

Bridging the gap between product lines and systems engineering: an experience in variability management for automotive model based systems engineering

We present in this paper an experience in modeling a family of parking brake systems, with shared assets and alternative solutions, and relate them to the needs of Renault in terms of variability management. The models are realized using a set of customized tools for model based systems engineering and variability management, based on SysML models. The purpose is to present an industrial context that requires the adoption of a product line approach and of variability modeling techniques, outside of a pure-software domain. At Renault, the interest is in identifying variations and reuse opportunities early in the product development cycle, as well as in preparing vehicle configuration specifications during the systems engineering process. This would lead to lowering the engineering effort and to higher quality and confidence in carry-over and carry across based solutions. We advocate for a tight integration of variability management with the model based systems engineering approach, which needs to address methodological support, modeling techniques and efficient tools for interactive configuration, adapted for engineering activities.

Using constraint programming to verify DOPLER variability models

Software product lines are typically developed using model-based approaches. Models are used to guide and automate key activities such as the derivation of products. The verification of product line models is thus essential to ensure the consistency of the derived products. While many authors have proposed approaches for verifying feature models there is so far no such approach for decision models. We discuss challenges of analyzing and verifying decision-oriented DOPLER variability models. The manual verification of these models is an error-prone, tedious, and sometimes infeasible task. We present a preliminary approach that converts DOPLER variability models into constraint programs to support their verification. We assess the feasibility of our approach by identifying defects in two existing variability models.

Feature Relations Graphs: A Visualisation Paradigm for Feature Constraints in Software Product Lines

Software Product Line Engineering is a mature approach enabling the derivation of product variants by assembling reusable assets. In this context, domain experts widely use Feature Models as the most accepted formalism for capturing commonality and variability in terms of features. Feature Models also describe the constraints in feature combinations. In industrial settings, domain experts often deal with Software Product Lines with high numbers of features and constraints. Furthermore, the set of features are often regrouped in different subsets that are overseen by different stakeholders in the process. Consequently, the management of the complexity of large Feature Models becomes challenging. In this paper we propose a dedicated interactive visualisation paradigm to help domain experts and stakeholders to manage the challenges in maintaining the constraints among features. We build Feature Relations Graphs (Frogs) by mining existing product configurations. For each feature, we are able to display a Frog which shows the impact, in terms of constraints, of the considered feature on all the other features. The objective is to help domain experts to 1) obtain a better understanding of feature constraints, 2) potentially refine the existing feature model by uncovering and formalizing missing constraints and 3) serve as a recommendation system, during the configuration of a new product, based on the tendencies found in existing configurations. The paper illustrates the visualisation paradigm with the industrial case study of Renaults Electric Parking System Software Product Line.

Recommendation Heuristics for Improving Product Line Configuration Processes

In mass customization industries, such as car manufacturing, configurators play an important role both to interact with customers and in engineering processes. This is particularly true when engineers rely on reuse of assets and product line engineering techniques. Theoretically, product line configuration should be guided by the product line model. However, in the industrial context, the configuration of products from product line models is complex and error-prone due to the large number of variables in the models. The configuration activity quickly becomes cumbersome due to the number of decisions needed to get a proper configuration, to the fact that they should be taken in predefined order, or the poor response time of configurators when decisions are not appropriate. This chapter presents a collection of recommendation heuristics to improve the interactivity of product line configuration so as to make it scalable to common engineering situations. We describe the principles, benefits, and the implementation of each heuristic using constraint programming. The application and usability of the heuristics is demonstrated using a case study from the car industry.