DIVERSIFY: Ecology-inspired software evolution for diversity emergence

update for BASE on Sep 08 2018 22:43:36International audienceDIVERSIFY is an EU funded project, which aims at favoring spontaneous diversification in software systems in order to increase their adaptive capacities. This objective is founded on three observations: software has to constantly evolve to face unpredictable changes in its requirements, execution environment or to respond to failure (bugs, attacks, etc.); the emergence and maintenance of high levels of diversity are essential to provide adaptive capacities to many forms of complex systems, ranging from ecological and biological systems to social and economical systems; diversity levels tend to be very low in software systems. DIVERSIFY explores how the biological evolutionary mechanisms, which sustain high levels of biodiversity in ecosystems (speciation, phenotypic plasticity and natural selection) can be translated in software evolution principles. In this work, we consider evolution as a driver for diversity as a means to increase resilience in software systems. In particular, we are inspired by bipartite ecological relationships to investigate the automatic diversification of the server side of a client-server architecture. This type of software diversity aims at mitigating the risks of software monoculture. The consortium gathers researchers from the software-intensive, distributed systems and the ecology areas in order to transfer ecological concepts and processes as software design principles

A Generative Middleware for Heterogeneous and Distributed Services

International audienceModern software-based services increasingly rely on a highly heterogeneous and dynamic interconnection of platforms and devices offering a wide diversity of capabilities ranging from cloud server with virtually unlimited resources down to micro-controllers with only a few KB of RAM. This paper motivates the fact that no single software framework or software engineering approach is suited to span across this range, and proposes an approach which leverages the latest advances in model-driven engineering, generative techniques and models@runtime in order to tame this tremendous heterogeneity. This paper presents a set of languages dedicated to the integration, deployment and continuous operation of existing libraries and components already available and implemented in various languages. The proposed approach is validated on an industrial case study in the eHealth domain, implemented by an industrial partner that provide an qualitative evaluation of the approach. This case study involves a large number of sensors, devices and gateways based on Rasperry Pi, Intel Edison and Arduino

A Generic Metamodel For Security Policies Mutation

International audienceWe present a new approach for mutation analysis of Security Policies test cases. We propose a metamodel that provides a generic representation of security policies access control models and define a set of mutation operators at this generic level. We use Kermeta to build the metamodel and implement the mutation operators. We also illustrate our approach with two successful instantiation of this metamodel: we defined policies with RBAC and OrBAC and mutated these policies

Model-based fleet deployment in the IoT–edge–cloud continuum

With the increasing computing and networking capabilities, IoT devices and edge gateways have become part of a larger IoT–edge–cloud computing continuum, where processing and storage tasks are distributed across the whole network hierarchy, not concentrated only in the cloud. At the same time, this also introduced continuous delivery practices to the development of software components for network-connected gateways and sensing/actuating nodes. These devices are placed on end users’ premises and are characterized by continuously changing cyber-physical contexts, forcing software developers to maintain multiple application versions and frequently redeploy them on a distributed fleet of devices with respect to their current contexts. Doing this correctly and efficiently goes beyond manual capabilities and requires an intelligent and reliable automated solution. This paper describes a model-based approach to automatically assigning multiple software deployment plans to hundreds of edge gateways and connected IoT devices implemented in collaboration with a smart healthcare application provider. From a platform-specific model of an existing edge computing platform, we extract a platform-independent model that describes a list of target devices and a pool of available deployment plans. Next, we use constraint solving to automatically assign deployment plans to devices at once with respect to their specific contexts. The result is transformed back into the platform-specific model and includes a suitable deployment plan for each device, which is then consumed by our engine to deploy software components not only on edge gateways but also on their downstream IoT devices with constrained resources and connectivity. We validate the approach with a fleet deployment prototype integrated into a DevOps toolchain used by the partner application provider. Initial experiments demonstrate the viability of the approach and its usefulness in supporting DevOps for edge and IoT software development.publishedVersio

Barriers to Systematic Model Transformation Testing

International audienceModel Driven Engineering (MDE) techniques support extensive use of models in order to manage the increasing complexity of software systems. Approp riate abstractions of software system elements can ease reasoning and understand ing and thus limit the risk of errors in large systems. Automatic model transfor mations play a critical role in MDE since they automate complex, tedious, error- prone, and recurrent software development tasks. A fault in a transformation can introduce a fault in the transformed model, which if undetected and not removed , can propagate to other models in successive development steps. As a fault prop agates further, it becomes more difficult to detect and isolate. Since model tra nsformations are meant to be reused, faults present in them may result in many f aulty models. Model transformations constitute a class of programs with unique c haracteristics that make testing them challenging. The complexity of input and o utput data, lack of model management tools, and the heterogeneity of transformat ion languages pose special problems to testers of transformations. In this paper we identify current model transformation characteristics that contribute to the difficulty of systematically testing transformations. We present promising solu tions and propose possible ways to overcome these barriers

Improveing test suites for efficient fault localization

ABSTRACT The need for testing-for-diagnosis strategies has been identified for a long time, but the explicit link from testing to diagnosis (fault localization) is rare. Analyzing the type of information needed for efficient fault localization, we identify the attribute (called Dynamic Basic Block) that restricts the accuracy of a diagnosis algorithm. Based on this attribute, a test-for-diagnosis criterion is proposed and validated through rigorous case studies: it shows that a test suite can be improved to reach a high level of diagnosis accuracy. So, the dilemma between a reduced testing effort (with as few test cases as possible) and the diagnosis accuracy (that needs as much test cases as possible to get more information) is partly solved by selecting test cases that are dedicated to diagnosis

Model-driven engineering for software migration in a large industrial context

Abstract. As development techniques, paradigms and platforms evolve far more quickly than domain applications, software modernization and migration, is a constant challenge to software engineers. For more than ten years now, the Sodifrance company has been intensively using ModelDriven Engineering (MDE) for both development and migration projects. In this paper we report on the use of MDE as an efficient, flexible and reliable approach for a migration process (reverse-engineering, transformation and code generation). Moreover, we discuss how MDE is economically profitable and is cost-effective over the migration through out-sourced manual re-development. The paper is illustrated with the migration of a large-scale banking system from Mainframe to J2EE

Improveing test suites for efficient fault localization

ABSTRACT The need for testing-for-diagnosis strategies has been identified for a long time, but the explicit link from testing to diagnosis (fault localization) is rare. Analyzing the type of information needed for efficient fault localization, we identify the attribute (called Dynamic Basic Block) that restricts the accuracy of a diagnosis algorithm. Based on this attribute, a test-for-diagnosis criterion is proposed and validated through rigorous case studies: it shows that a test suite can be improved to reach a high level of diagnosis accuracy. So, the dilemma between a reduced testing effort (with as few test cases as possible) and the diagnosis accuracy (that needs as much test cases as possible to get more information) is partly solved by selecting test cases that are dedicated to diagnosis

Model-Driven Engineering for Software Migration in a Large Industrial Context

International audienceAs development techniques, paradigms and platforms evolve far more quickly than domain applications, software modernization and migration, is a constant challenge to software engineers. For more than ten years now, the Sodifrance company has been intensively using Model-Driven Engineering (MDE) for both development and migration projects. In this paper we report on the use of MDE as an efficient, flexible and reliable approach for a migration process (reverse-engineering, transformation and code generation). Moreover, we discuss how MDE is economically profitable and is cost-effective over the migration through out-sourced manual re-development. The paper is illustrated with the migration of a large-scale banking system from Mainframe to J2EE

Modeling Context and Dynamic Adaptations with Feature Models

International audienceSelf-adaptive and dynamic systems adapt their behavior according to the context of execution. The contextual information exhibits multiple variability factors which induce many possible configurations of the software system at runtime. The challenge is to specify the adaptation rules that can link the dynamic variability of the context with the possible variants of the system. Our work investigates the systematic use of feature models for modeling the context and the software variants, together with their inter relations, as a way to configure the adaptive system with respect to a particular context. A case study in the domain of video surveillance systems is used to illustrate the approach