International audience

Until recently, the state-of-the-art of Software Product Line (SPL) configuration and verification automation consisted of a collection of ad-hoc approaches tightly coupling a single input Variability Modeling Language (VML) with a single constraint solver. To remedy this situation, a novel generic model-driven architecture was then proposed that enables using a variety of VMLs and solvers. The key ideas of this proposal were (a) the use of a standard logical language (CLIF) as a pivot between VMLs and solvers, and (b) the use of a standard data exchange format (JSON) to explicilty and declaratively specify the abstract syntax and semantics of the VMLs to be used in an SPL engineering project and the automated reasoning task to be performed by the solvers.

Verifying and configuring large Software Product Lines (SPL) requires automation tools. Current state-of-the-art approaches involve translating variability models into a formalism accepted as input by a constraint solver. There are currently no standards for variability modeling languages (VML). There is also a variety of constraint solver input languages. This has resulted in a multiplication of ad-hoc architectures and tools specialized for a single pair of VML and solver, fragmenting the SPL community. To overcome this limitation, we propose a novel architecture based on model-driven code generation, where the syntax and semantics of VMLs can be declaratively specified as data, and a standard, human-readable, formal pivot language is used between the VML and the solver input language. This architecture is the first to be fully generic by being agnostic to both VML and the solver paradigm. To validate the genericity of the approach, we have implemented a prototype tool together with declarative specifications for the syntax and semantics of two different VMLs and two different solver families. One VML is for classic, static SPL, and the other for run-time reconfigurable dynamic SPL with soft constraints to be optimized during configuration. The two solver families are Constraint Satisfaction Programs (CSP) and Constraint Logic Programs (CLP).

With the popularity of software defined networking architectures, the growing complexity of its use cases dictates the need for better auditability especially for security. In this paper, we aim at facilitating high-level management-plane policy configuration conformance auditing and their reflection in the data plane, to detect missing or spurious flow rules with respect to security policies. To this end, we propose an efficient conformance checking approach based on an intentional northbound interface as well as traces of management, control and data plane. Leveraging a proof-of-concept implementation of our approach, we compare its conformance-checking runtime and precision against a direct method on virtual topologies and find that it significantly improves scalability. We conclude by proposing directions for further enhancements extending the techniques presented herein.

With the ever growing digitization of activities, software systems are getting more and more complex. They must comply with new usages, varied needs, and are permanently exposed to new security vulnerabilities.

In the context of Product lines, the collaborative configuration process gets complicated when the configuration decisions of involved stakeholders are contradictory, which may lead to conflicting situations. Although considerable research has been devoted to collaborative configuration, little attention has been paid to conflict resolution. Moreover, most of existing approaches rely on a systematic process which constraint decisions of some stakeholders. In this paper, we propose a new collaborative configuration approach which allows conflict resolution based on stakeholders preferences expressed through a set of substitution rules. Based on such preferences, we delete the minimal set of conflicting configuration decisions which are identified using the Minimal Correction Subsets (MCSs) computing algorithm. An illustrating example and a tool prototype are presented to evaluate the applicability of our approach.

Cybersecurity is becoming vital as industries are gradually moving from automating physical processes to a higher level automation using cyber physical systems (CPS) and internet of things (IoT). In this context, security is becoming a continuous process that runs in parallel to other processes during the complete life cycle of a system. Traditional threat analysis methods use design models alongside threat models as an input for security analysis, hence missing the life-cycle-based dynamicity required by the security concern. In this paper, we argue for an attacker-aware systems modeling language that exposes the systems attack surfaces. For this purpose, we have designed Pimca, a domain specific modeling language geared towards capturing the attacker point of view of the system. This study introduces the formalism along with the Pimca workbench, a framework designed to ease the development and manipulation of the Pimca models. Finally, we present two relevant use cases, serving as a preliminary validation of our approach. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

The demand for systems that continue on operation by adapting themselves in response to disturbing changes in their environment has increased in the last decades. Those systems, termed self-adaptive software (SAS) systems, should be developed with techniques and methods appropriated for analysing and designing this kind of systems, starting from the requirements phase. Several contributions propose approaches to improve the specification of requirements for those systems. This paper aims to review the most significant challenges still open in the domains of languages for requirements specification and methods for model verification of self-adaptive systems, independently of their particular application areas. More concretely, the main contribution of this paper is a list of ten challenges to achieve a better-defined specification of requirements for SAS systems, and a more effective verification of such specifications. These challenges are well worthy of being addressed in both communities, the requirements engineering (RE) and the SAS one.

In the software industry, product Lines has emerged as an attractive approach to perform planned reuse. Nevertheless, a product line solution is not appropriate for all the cases and requires some conditions to be implemented successfully. The literature offers several contributions regarding the adoption of product lines. However, only a few of these support decision-makers for taking well-informed decisions in favor or against following this approach. This paper presents APPLIES, a framework to (i) detect signals that could motivate an organization to adopt a product line approach (ii) assess to what extent a company is prepared for supporting the practices necessary to adopt a product line. APPLIES is grounded in a thematic analysis of the literature and was preliminarily evaluated with two experts through a survey and an interview. The results of the evaluation provide us insights to create an improved version of the proposal following a design-science method.

There are several proposals to build models feature either semi-automatic or fully automatic in the field of Software Product Lines. Unfortunately, there is no clarity on the common elements, their main differences and elements that characterize these proposals. The wide variety of terms used to refer to the process of building a feature model (e.g. synthesis, location, re engineering) makes the proposals are varied and very heterogeneous, which makes it complex to understand and classify them. This paper introduces a conceptual framework for classifying and comparing proposals related to the construction of feature models by semi-automatic or fully automatic means. The proposal considers six dimensions mainly focused on characterizing the input sources, methods and techniques, the results and the type of evaluation. The conceptual framework provides guidance to researchers for choosing the appropriate aspects to build feature models and helps to understand and clarify the various approaches proposed in the literature.

Context: Organizations act in highly competitive markets, which forces them to be flexible. Constantly changing business requirements require flexible business processes. Case Management Model and Notation (CMMN) supports modeling run-time flexibility of partially structured business process models, but does not fully specify the control flow. Objective: The goal is to develop a planning algorithm that supports the case worker in planning case-based business processes at run-time. Method: We identify the requirements of run-time planning of partly structured processes by analyzing the admission process for the master degree at FHNW. To plan the process instance, we develop a planning algorithm. Our planning algorithm is evaluated using concrete cases provided by FHNW in order to demonstrate real application. Results: The planning algorithm reflects the requirements for serializing tasks at run-time. Conclusion: Our planning algorithm allows to automatically deriving context-specific execution plans for CMMN models at run-time.

E-learning is a promising research area, as they are expected to increase enrollment and improve the quality of education. Adaptive e-learning systems, traditionally focused on content personalization, are in need to cope with continuous changing requirements and changing environment. Indeed, the specification and the management quality attributes of such systems, supported throughout the whole lifecycle are still missing. In this paper, we propose continuous requirements monitoring that uses a constraint program to check the conformity of adaptive e-learning systems to their requirements and react properly when deviations occur at runtime. To this end, we specify system's requirements in the form of a dynamic software product line. A novel requirements engineering language that combines goal-driven requirements with features and claims is applied for the specification, from which the constraint program is automatically generated.

Product line engineering uses product line models to define the valid combinations of elements in a product and to configure them. Several modeling languages have been proposed to represent product line models. These languages have limits and they do not always fit the requirements of the context. For instance, in the industrial context, product lines contain often multi-instantiated features which are more difficult to model. This paper reports lessons learned during a project with an electronic supplier company called Rexel. Our objective in this study was to find out the appropriate modeling languages to model multi-instantiation, as required the Rexel's Electric Board that contains many multi-instantiated features. This paper presents (i) how three categories of modeling languages have been used in industry to model the multi-instantiation concept of an electric board; (ii) the limits and difficulties encountered with each category of modeling languages; and (iii) modeling strategies to handle multi-instantiation with each kind of language.

This paper presents the new release of VariaMos, a Java-based tool for defining variability modeling languages, modeling (dynamic) product lines and cyber-physical self-adaptive systems, and supporting automated verification, analysis, configuration and simulation of these models. In particular, we describe the characteristics of this new version regarding its first release: (1) the capability to create languages for modeling systems with variability, even with different views; (2) the capability to use the created language to model (dynamic) product lines; (3) the capability to analyze and configure these models according to the changing context and requirements; and (4) the capability to execute them over several simulation scenarios. Finally, we show how to use VariaMos with an example, and we compare it with other tools found in the literature.

Model simulation has demonstrated its usefulness in evaluation and decision-making for improving preliminary versions of artefacts before production. Particularly, one of the main goals of simulation is to verify model properties based on data collected from its execution. In this paper, we present the simulation capabilities of our REFAS framework for specifying requirements models for dynamic software products lines and self-adaptive systems. The simulation is controlled by a feedback loop and a reasoning engine that operates on the functional and non-functional requirements. The paper contribution is threefold. First, REFAS allows developers to evaluate and improve requirements models through their simulation capabilities. Second, REFAS provides rich feedback in its interactive simulations for the human modeller to make informed decisions to improve her model. Third, REFAS automates the generation of simulation scenarios required to verify the model adequacy and correctness. We evaluate our contribution by comparing the application of REFAS to a case study used in other approaches.

The research on requirements specification for self-adaptive systems has a growing interest in the academy and the industry. As a result, currently there exists different proposals for the specification of requirements for self-adaptive systems. Despite the momentum that this area has received in recent years, in the works reported in the literature we have identified shortcomings. We propose a new framework to represent the requirements of self-adaptive systems. This framework seeks to manage uncertainty and to be sufficiently expressive for self-adaptive systems, including the representation of all the relevant concepts. The concepts, represented in different views to be used in a 5-stage process. Specifically, we present: (i) a discussion of the challenges and problems encountered in the literature; (ii); our proposal to solve these challenges; and (iii) a case study of the problem and its application.

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 Renault's Electric Parking System Software Product Line.

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.

Developing high quality systems depends on developing high quality models. An important facet of model quality is their consistency with respect to their meta-model. We call the verification of this quality the conformance checking process. We are interested in the conformance checking of Product Line Models (PLMs). The problem in the context of product lines is that product models are not created by instantiating a meta-model: they are derived from PLMs. Therefore it is usually at the level of PLMs that conformance checking is applied. On the semantic level, a PLM is defined as the collection of all the product models that can be derived from it. Therefore checking the conformance of the PLM is equivalent to checking the conformance of all the product models. However, we would like to avoid this naïve approach because it is not scalable due to the high number of models. In fact, it is even sometimes infeasible to calculate the number of product models of a PLM. Despite the importance of PLM conformance checking, very few research works have been published and tools do not adequately support it. In this paper, we present an approach that employs Constraint Logic Programming as a technology on which to build a PLM conformance checking solution. The paper demonstrates the approach with feature models, the de facto standard for modeling software product lines. Based on an extensive literature review and an empirical study, we identified a set of 9 conformance checking rules and implemented them on the GNU Prolog constraints solver. We evaluated our approach by applying our rules to 50 feature models of sizes up to 10000 features. The evaluation showed that our approach is effective and scalable to industry size models.

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.

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.

Constraint Programming (CP), and in particular boolean CP, has been used so far to support analysis of variability models such as Feature Oriented Domain Analysis (FODA) and like. This paper goes a step further by using constraint programming to specify the Product Line (PL). The vision of variability and variation points completely disappear, and the focus is completely on constraints. Of course, the constraints can originate from various PL models, which can still be used to guide product analysis. However, owing to the unique constraint program, the analysis of the different viewpoints represented by each of these models is achieved in an integrated way.

Product line models (PLM) are important artifacts in product line engineering. Due to their size and complexity, it is difficult to detect defects in PLMs. The challenge is however important: any error in a PLM will inevitably impact configuration, generating issues such as incorrect product models, inconsistent architectures, poor reuse, difficulty to customize products, etc. Surveys on feature-based PLM verification approaches show that there are many verification criteria, that these criteria are defined in different ways, and that different ways of working are proposed to look for defect. The goal of this paper is to systematize PLM verification. Based on our literature review, we propose a list of 23 verification criteria that we think cover those available in the literature.