With the rise of AI-based code generation, customizing existing code out of natural language instructions to modify visual results – such as figures or images – has become possible, promising to reduce the need for deep programming expertise. However, even experienced developers can struggle with this task, as it requires identifying relevant code regions (feature location), generating valid code variants, and ensuring the modifications reliably align with user intent. In this paper, we introduce vTikZ, the first benchmark designed to evaluate the ability of Large Language Models (LLMs) to customize code while preserving coherent visual outcomes. Our benchmark consists of carefully curated vTikZ editing scenarios, parameterized ground truths, and a reviewing tool that leverages visual feedback to assess correctness. Empirical evaluation with state-of-the-art LLMs shows that existing solutions struggle to reliably modify code in alignment with visual intent, highlighting a gap in current AI-assisted code editing approaches. We argue that vTikZ opens new research directions for integrating LLMs with visual feedback mechanisms to improve code customization tasks in various domains beyond TikZ, including image processing, art creation, Web design, and 3D modeling.

Context.In Software Engineering, Model-Driven Engineering (MDE) is a methodology that considers Metamodels as a cornerstone. As an abstract artifact, a metamodel plays a significant role in the specification of a software language, particularly, in generating other artifacts of lower abstraction level, such as code. Developers then enrich the generated code to build their language services and tooling, e.g., editors, and checkers.Problem.When a metamodel evolves, the generated code is automatically updated. As a consequence, the developers’ additional code is impacted and needs to be co-evolved accordingly.Contribution.This paper proposes a new fully automatic code co-evolution approach with the evolution of the Ecore metamodel. The approach relies on pattern matching of the additional code errors. This process aims to analyze the abstraction gap between the evolved metamodel elements and the code errors to co-evolve them.Evaluation and Results.We evaluated our approach on nine Eclipse projects from OCL, Modisco, and Papyrus over several evolved versions of three metamodels. Results show that we automatically co-evolved 771 errors due to metamodel evolution with 631 matched and applied resolutions. Our approach reached an average of 82% of precision and 81% of recall, varying from 48% to 100% for precision and recall respectively. To check the effect of the co-evolution and its behavioral correctness, we rely on generated test cases before and after co-evolution. We observed that the percentage of passing, failing, and erroneous tests remained the same with insignificant variations in some projects. Thus, suggesting the behavioral correctness of the co-evolution Moreover, we conducted a comparison with the use of quick fixes that represent a usual tool for correcting code errors in an IDE. We found that our automatic co-evolution approach outperforms the use of quick fixes that lacked the context of metamodel evolution. Finally, we also compared our approach with the state-of-the-art semi-automatic co-evolution approach. As expected, precision and recall are slightly better with semi-automation, but with the burden of manual intervention, which is alleviated with our automatic co-evolution.

Intrusion Detection Systems (IDS) are time-sensitive applications that aim to classify potentially malicious network traffic. IDSs are part of a class of applications that rely on short-lived functions that can be run reactively and, as such, could be deployed on edge resources, to offload processing from energy-constrained battery-backed devices. The serverless service model could fit the needs of such applications, given that the platform allows adequate levels of Quality of Service (QoS) for a variety of users, since the criticality of IDS applications depends on several parameters. Deploying serverless functions on unreserved edge resources requires to pay particular attention to (1) initialization delays that could be significant on low resources platforms, (2) inter-function communication between edge nodes, and (3) heterogeneous devices. In this paper, we propose both a storage-aware allocation and scheduling policy that seek to minimize task placement costs for service providers on edge devices while optimizing QoS for IDS users. To do so, we propose a caching and consolidation strategy that minimizes cold starts and inter-function communication delays while satisfying QoS by leveraging heterogeneous edge resources. We evaluated our platform in a simulation environment using characterization data from real-world IDS tasks and execution platforms and compared it with a vanilla Knative orchestrator and a storage-agnostic policy. Our strategy achieves 18% fewer QoS penalties while consolidating applications across 80% fewer edge nodes.

Current software supply chains heavily rely on open-source packages hosted in public repositories. Given the popularity of ecosystems like npm and PyPI, malicious users started to spread malware by publishing open-source packages containing malicious code.

Serverless is a trending service model for cloud computing. It shifts a lot of the complexity from customers to service providers. However, current serverless platforms mostly consider the provider's infrastructure as homogeneous, as well as the users' requests. This limits possibilities for the provider to leverage heterogeneity in their infrastructure to improve function response time and reduce energy consumption. We propose a heterogeneity-aware serverless orchestrator for private clouds that consists of two components: the autoscaler allocates heterogeneous hardware resources (CPUs, GPUs, FPGAs) for function replicas, while the scheduler maps function executions to these replicas. Our objective is to guarantee function response time, while enabling the provider to reduce resource usage and energy consumption. This work considers a case study for a deepfake detection application relying on CNN inference. We devised a simulation environment that implements our model and a baseline Knative orchestrator, and evaluated both policies with regard to consolidation of tasks, energy consumption and SLA penalties. Experimental results show that our platform yields substantial gains for all those metrics, with an average of 35% less energy consumed for function executions while consolidating tasks on less than 40% of the infrastructure's nodes, and more than 60% less SLA violations.

Software systems evolve more and more in complex and changing environments, often requiring runtime adaptation to best deliver their services. When self-adaptation is the main concern of the system, a manual implementation of the underlying feedback loop and trade-off analysis may be desirable. However, the required expertise and substantial development effort make such implementations prohibitively difficult when it is only a secondary concern for the given domain. In this paper, we present ASOS, a metalanguage abstracting the runtime adaptation concern of a given domain in the behavioral semantics of a domain-specific language (DSL), freeing the language user from implementing it from scratch for each system in the domain. We demonstrate our approach on RobLANG, a procedural DSL for robotics, where we abstract a recurrent energy-saving behavior depending on the context. We provide formal semantics for ASOS and pave the way for checking properties such as determinism, completeness, and termination of the resulting self-adaptable language. We provide first results on the performance of our approach compared to a manual implementation of this self-adaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient.

The widespread dependency on open-source software makes it a fruitful target for malicious actors, as demonstrated by recurring attacks. The complexity of today’s open-source supply chains results in a significant attack surface, giving attackers numerous opportunities to reach the goal of injecting malicious code into open-source artifacts that is then downloaded and executed by victims.This work proposes a general taxonomy for attacks on open-source supply chains, independent of specific programming languages or ecosystems, and covering all supply chain stages from code contributions to package distribution. Taking the form of an attack tree, it covers 107 unique vectors, linked to 94 real-world incidents, and mapped to 33 mitigating safeguards.User surveys conducted with 17 domain experts and 134 software developers positively validated the correctness, comprehensiveness and comprehensibility of the taxonomy, as well as its suitability for various use-cases. Survey participants also assessed the utility and costs of the identified safeguards, and whether they are used.

In many situations, it is of interest for authentication systems to adapt to context (e.g., when the user’s behavior differs from the previous behavior). Hence, representing the context with appropriate and well-designed models is crucial. We provide a comprehensive overview and analysis of research work on C ontext M odelling f or A daptive A uthentication systems (CM4AA). To this end, we pursue three goals based on the Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) research methodologies. We first present a SMS to structure the research area of CM4AA ( goal 1 ). We complement the SMS with an SLR to gather and synthesise evidence about context information and its modelling for adaptive authentication systems ( goal 2 ). From the knowledge gained from goal 2, we determine the desired properties of the context information model and its use for adaptive authentication systems ( goal 3 ). Motivated to find out how to model context information for adaptive authentication, we provide a structured survey of the literature to date on CM4AA and a classification of existing proposals according to several analysis metrics. We demonstrate the ability of capturing a common set of contextual features that are relevant for adaptive authentication systems independent from the application domain. We emphasise that despite the possibility of a unified framework, no standard for CM4AA exists.

Nowadays, many mechanisms exist to perform authentication, such as text passwords and biometrics. However, reasoning about their relevance (e.g., the appropriateness for security and usability) regarding the contextual situation is challenging for authentication system designers. In this paper, we present a Context-driven Modelling Framework for dynamic Authentication decisions (COFRA), where the context information specifies the relevance of authentication mechanisms. COFRA is based on a precise metamodel that reveals framework abstractions and a set of constraints that specify their meaning. Therefore, it provides a language to determine the relevant authentication mechanisms (characterized by properties that ensure their appropriateness) in a given context. The framework supports the adaptive authentication system designers in the complex trade-off analysis between context information, risks and authentication mechanisms, according to usability, deployability, security, and privacy. We validate the proposed framework through case studies and extensive exchanges with authentication and modelling experts. We show that model instances describing real-world use cases and authentication approaches proposed in the literature can be instantiated validly according to our metamodel. This validation highlights the necessity, sufficiency, and soundness of our framework.

In this paper, we propose RISCLESS, a Reinforcement Learning strategy to exploit unused Cloud resources. Our approach consists in using a small proportion of stable on-demand resources alongside the ephemeral ones in order to guarantee customers SLA and reduce the overall costs. The approach decides when and how much stable resources to allocate in order to fulfill customers’ demands. RISCLESS improved the Cloud Providers (CPs)’ profits by an average of 15.9% compared to past strategies. It also reduced the SLA violation time by 36.7% while increasing the amount of used ephemeral resources by 19.5%.

In many situations, it is of interest for authentication systems to adapt to context (e.g., when the user's behavior differs from the previous behavior). Hence, during authentication events, it is common to use contextually available features to calculate an impersonation risk score. This paper proposes an explainability model that can be used for authentication decisions and, in particular, to explain the impersonation risks that arise during suspicious authentication events (e.g., at unusual times or locations). The model applies Shapley values to understand the context behind the risks. Through a case study on 30,000 real world authentication events, we show that risky and non-risky authentication events can be grouped according to similar contextual features, which can explain the risk of impersonation differently and specifically for each authentication event. Hence, explainability models can effectively improve our understanding of impersonation risks. The risky authentication events can be classified according to attack types. The contextual explanations of the impersonation risk can help authentication policymakers and regulators who attempt to provide the right authentication mechanisms, to understand the suspiciousness of an authentication event and the attack type, and hence to choose the suitable authentication mechanism.

Exploratory programming is a software development style in which code is a medium for prototyping ideas and solutions, and in which even the end-goal can evolve over time. Exploratory programming is valuable in various contexts such as programming education, data science, and end-user programming. However, there is a lack of appropriate tooling and language design principles to support exploratory programming. This paper presents a host language- and object language-independent protocol for exploratory programming akin to the Language Server Protocol. The protocol serves as a basis to develop novel (or extend existing) programming environments for exploratory programming such as computational notebooks and command-line REPLs. An architecture is presented on top of which prototype environments can be developed with relative ease, because existing (language) components can be reused. Our prototypes demonstrate that the proposed protocol is sufficiently expressive to support exploratory programming scenarios as encountered in literature within the software engineering, human-computer interaction and data science domains.

Detecting faults in distributed network systems is challenging because of their complexity, but this is required to evaluate and improve their reliability. This paper proposes ChaT, a testing and evaluation methodology under system reconfigurations and perturbations for distributed network systems, to evaluate QoS reliability by discriminating safe and failure-prone behaviors from different testing scenarios. Motivated by meta-morphic testing technique that removes the burden of defining software oracles, we propose some metamorphic relationships that correlate system inputs and outputs to find patterns in executions. Classification techniques based on machine learning (principal component analysis and support vector machine) are used to identify system states and validate the proposed metamorphic relationships. These metamorphic relationships are also used to help anomaly detection. We verify this with several anomaly detection techniques (isolation forest, one-class SVM, local outlier factor, and robust covariance) that categorize experiments belonging to either safe or failure-prone states. We apply ChaT to a video streaming application use case. The simulation results show the effectiveness of ChaT to achieve our goals: identifying execution classes and detecting failure-prone experiments based on metamorphic relationships with high level of statistical scores.

Over recent years, self-adaptation has become a major concern for software systems that evolve in changing environments. While expert developers may choose a manual implementation when self-adaptation is the primary concern, self-adaptation should be abstracted for non-expert developers or when it is a secondary concern. We present SEALS, a framework for building self-adaptive virtual machines for domain-specific languages. This framework provides first-class entities for the language engineer to promote domain-specific feedback loops in the definition of the DSL operational semantics. In particular, the framework supports the definition of (i) the abstract syntax and the semantics of the language as well as the correctness envelope defining the acceptable semantics for a domain concept, (ii) the feedback loop and associated trade-off reasoning, and (iii) the adaptations and the predictive model of their impact on the trade-off. We use this framework to build three languages with self-adaptive virtual machines and discuss the relevance of the abstractions, effectiveness of correctness envelopes, and compare their code size and performance results to their manually implemented counterparts. We show that the framework provides suitable abstractions for the implementation of self-adaptive operational semantics while introducing little performance overhead compared to a manual implementation.

One of the cornerstones of the cloud provider business is to reduce hardware resources cost by maximizing their utilization. This is done through smartly sharing processor, memory, network and storage, while fully satisfying SLOs negotiated with customers. For the storage part, while SSDs are increasingly deployed in data centers mainly for their performance and energy efficiency, their internal mechanisms may cause a dramatic SLO violation. In effect, we measured that I/O interference may induce a 10x performance drop. We are building a framework based on autonomic computing which aims to achieve intelligent container placement on storage systems by preventing bad I/O interference scenarios. One prerequisite to such a framework is to design SSD performance models that take into account interactions between running processes/containers, the operating system and the SSD. These interactions are complex. In this paper, we investigate the use of machine learning for building such models in a container based Cloud environment. We have investigated five popular machine learning algorithms along with six different I/O intensive applications and benchmarks. We analyzed the prediction accuracy, the learning curve, the feature importance and the training time of the tested algorithms on four different SSD models. Beyond describing modeling component of our framework, this paper aims to provide insights for cloud providers to implement SLO compliant container placement algorithms on SSDs. Our machine learning-based framework succeeded in modeling I/O interference with a median Normalized Root-Mean-Square Error (NRMSE) of 2.5 percent.

Cloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources (referred to as ephemeral resources) at a lower price. However, reselling resources needs to meet the expectations of its customers in terms of Quality of Service. The goal is so to maximize the amount of reclaimed resources while avoiding SLA penalties. To achieve that, cloud providers have to estimate their future utilization to provide availability guarantees. The prediction should consider a safety margin for resources to react to unpredictable workloads. The challenge is to find the safety margin that provides the best trade-off between the amount of resources to reclaim and the risk of SLA violations. Most state-of-the-art solutions consider a fixed safety margin for all types of metrics (e.g., CPU, RAM). However, a unique fixed margin does not consider various workloads variations over time which may lead to SLA violations or/and poor utilization. In order to tackle these challenges, we propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. The strategy learns from past prediction errors (that caused SLA violations). Our solution reduces significantly the SLA violation penalties on average by 2.7× and up to 3.4×. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%.

Metamodels play a significant role to describe and analyze the relations between domain concepts. They are also cornerstone to build a software language (SL) for a domain and its associated tooling. Metamodel definition generally drives code generation of a core API. The latter is further enriched by developers with additional code implementing advanced functionalities, e.g., checkers, recommenders, etc. When a SL is evolved to the next version, the metamodels are evolved as well before to re-generate the core API code. As a result, the developers added code both in the core API and the SL toolings may be impacted and thus may need to be co-evolved accordingly. Many approaches support the co-evolution of various artifacts when metamodels evolve. However, not the co-evolution of code. This paper fills this gap. We propose a semi-automatic co-evolution approach based on change propagation. The premise is that knowledge of the metamodel evolution changes can be propagated by means of resolutions to drive the code co-evolution. Our approach leverages on the abstraction level of metamodels where a given metamodel element has often different usages in the code. It supports alternative co-evaluations to meet different developers needs. Our work is evaluated on three Eclipse SL implementations, namely OCL, Modisco, and Papyrus over several evolved versions of metamodels and code. In response to five different evolved metamodels, we co-evolved 976 impacts over 18 projects.A comparison of our co-evolved code with the versioned ones shows the usefulness of our approach. Our approach was able to reach a weighted average of 87.4% and 88.9% respectively of precision and recall while supporting useful alternative co-evolution that developers have manually performed.

Integrated Development Environments (IDEs) are indispensable companions to programming languages. They are increasingly turning towards Web-based infrastructure. The rise of a protocol such as the Language Server Protocol (LSP) that standardizes the separation between a language-agnostic IDE, and a language server that provides all language services (e.g., auto completion, compiler...) has allowed the emergence of high quality generic Web components to build the IDE part that runs in the browser. However, all language services require different computing capacities and response times to guarantee a user-friendly experience within the IDE. The monolithic distribution of all language services prevents to leverage on the available execution platforms (e.g., local platform, application server, cloud). In contrast with the current approaches that provide IDEs in the form of a monolithic client-server architecture, we explore in this paper the modularization of all language services to support their individual deployment and dynamic adaptation within an IDE. We evaluate the performance impact of the distribution of the language services across the available execution platforms on four EMF-based languages, and demonstrate the benefit of a custom distribution.

SUMMARY Generative software development has paved the way for the creation of multiple code generators that serve as a basis for automatically generating code to different software and hardware platforms. In this context, the software quality becomes highly correlated to the quality of code generators used during software development. Eventual failures may result in a loss of confidence for the developers, who will unlikely continue to use these generators. It is then crucial to verify the correct behaviour of code generators in order to preserve software quality and reliability. In this paper, we leverage the metamorphic testing approach to automatically detect inconsistencies in code generators via so‐called “metamorphic relations”. We define the metamorphic relation (i.e., test oracle) as a comparison between the variations of performance and resource usage of test suites running on different versions of generated code. We rely on statistical methods to find the threshold value from which an unexpected variation is detected. We evaluate our approach by testing a family of code generators with respect to resource usage and performance metrics for five different target software platforms. The experimental results show that our approach is able to detect, among 95 executed test suites, 11 performance and 15 memory usage inconsistencies.

Cloud infrastructures are generally over-provisioned for handling load peaks and node failures. However, the drawback of this approach is that a large portion of data center resources remains unused. In this paper, we propose a framework that leverages unused resources of data centers, which are ephemeral by nature, to run MapReduce jobs. Our approach allows: i) to run efficiently Hadoop jobs on top of heterogeneous Cloud resources, thanks to our data placement strategy, ii) to predict accurately the volatility of ephemeral resources, thanks to the quantile regression method, and iii) for avoiding the interference between MapReduce jobs and co-resident workloads, thanks to our reactive QoS controller. We have extended Hadoop implementation with our framework and evaluated it with three different data center workloads. The experimental results show that our approach divides Hadoop job execution time by up to 7 when compared to the standard Hadoop implementation.

Companies are more and more inclined to use collaborative cloud resources when their maximum internal capacities are reached in order to minimize their TCO. The downside of using such a collaborative cloud, made of private clouds' unused resources, is that malicious resource providers may sabotage the correct execution of third-party-owned applications due to its uncontrolled nature. In this paper, we propose an approach that allows sabotage detection in a trustless environment. To do so, we designed a mechanism that (1) builds an application fingerprint considering a large set of resources usage (such as CPU, I/O, memory) in a trusted environment using random forest algorithm, and (2) an online remote fingerprint recognizer that monitors application execution and that makes it possible to detect unexpected application behavior. Our approach has been tested by building the fingerprint of 5 applications on trusted machines. When running these applications on untrusted machines (with either homogeneous, heterogeneous or unspecified hardware from the one that was used to build the model), the fingerprint recognizer was able to ascertain whether the execution of the application is correct or not with a median accuracy of about 98% for heterogeneous hardware and about 40% for the unspecified one.

In this demo paper, we present an architecture that leverages unused but volatile Cloud resources to run big data jobs. It is based on a learning algorithm that accurately predicts future availability of resources to automatically scale the ran jobs. We also designed a mechanism that avoids interference between the Big data jobs and co-resident workloads. Our solution is based on Open-Source components such as kubernetes and Apache Spark.

The Maven Central Repository provides an extraordinary source of data to understand complex architecture and evolution phenomena among Java applications. As of September 6, 2018, this repository includes 2.8M artifacts (compiled piece of code implemented in a JVM-based language), each of which is characterized with metadata such as exact version, date of upload and list of dependencies towards other artifacts. Today, one who wants to analyze the complete ecosystem of Maven artifacts and their dependencies faces two key challenges: (i) this is a huge data set; and (ii) dependency relationships among artifacts are not modeled explicitly and cannot be queried. In this paper, we present the Maven Dependency Graph. This open source data set provides two contributions: a snapshot of the whole Maven Central taken on September 6, 2018, stored in a graph database in which we explicitly model all dependencies; an open source infrastructure to query this huge dataset.

Maven artifacts are immutable: an artifact that is uploaded on Maven Central cannot be removed nor modified. The only way for developers to upgrade their library is to release a new version. Consequently, Maven Central accumulates all the versions of all the libraries that are published there, and applications that declare a dependency towards a library can pick any version. In this work, we hypothesize that the immutability of Maven artifacts and the ability to choose any version naturally support the emergence of software diversity within Maven Central. We analyze 1,487,956 artifacts that represent all the versions of 73,653 libraries. We observe that more than 30% of libraries have multiple versions that are actively used by latest artifacts. In the case of popular libraries, more than 50% of their versions are used. We also observe that more than 17% of libraries have several versions that are significantly more used than the other versions. Our results indicate that the immutability of artifacts in Maven Central does support a sustained level of diversity among versions of libraries in the repository.

The adoption of Domain-Specific Languages (DSLs) relies on the capacity of language workbenches to automate the development of advanced and customized environments. While DSLs are usually well tailored for the main scenarios, the cost of developing mature tools prevents the ability to develop additional capabilities for alternative scenarios targeting specific tasks (e.g., API testing) or stakeholders (e.g., education). In this paper, we propose an approach to automatically generate interactive computer programming environments from existing specifications of textual interpreted DSLs. The approach provides abstractions to complement the DSL specification, and combines static analysis and language transformations to automate the transformation of the language syntax, the execution state and the execution semantics. We evaluate the approach over a representative set of DSLs, and demonstrate the ability to automatically transform a textual syntax to load partial programs limited to a single statement, and to derive a Read-Eval-Print-Loop (REPL) from the specification of a language interpreter.

Although Cloud computing techniques have reduced the total cost of ownership thanks to virtualization, the average usage of resources (e.g., CPU, RAM, Network, I/O) remains low. To address such issue, one may sell unused resources. Such a solution requires the Cloud provider to determine the resources available and estimate their future use to provide availability guarantees. This paper proposes a technique that uses machine learning algorithms (Random Forest, Gradient Boosting Decision Tree, and Long Short Term Memory) to forecast 24-hour of available resources at the host level. Our technique relies on the use of quantile regression to provide a flexible trade-off between the potential amount of resources to reclaim and the risk of SLA violations. In addition, several metrics (e.g., CPU, RAM, disk, network) were predicted to provide exhaustive availability guarantees. Our methodology was evaluated by relying on four in production data center traces and our results show that quantile regression is relevant to reclaim unused resources. Our approach may increase the amount of savings up to 20% compared to traditional approaches.

Self-consumption of renewable energies is defined as electricity that is produced from renewable energy sources, not injected to the distribution or transmission grid or instantaneously withdrawn from the grid and consumed by the owner of the power production unit or by associates directly contracted to the producer. Designing solutions in favor of self-consumption for small industries or city districts is challenging. It consists in designing an energy production system made of solar panels, wind turbines, batteries that fit the annual weather prediction and the industrial or human activity. In this context, this paper reports the context of this business domain, its challenges, and the application of modeling that leads to a solution. Through this article, we highlight the essentials of a domain specific modeling language designed to let domain experts run their own simulations, we compare with existing practices that exist in such a company and we discuss the benefits and the limits of the use of modeling in such context.

The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mobilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today's applications are dealing with is inherently temporal, current approaches, methodologies, and environments for developing these applications don't provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain.

Executable Domain-Specific Modeling Languages (xDSMLs) are typically defined by metamodels that specify their abstract syntax, and model interpreters or compilers that define their execution semantics. To face the proliferation of xDSMLs in many domains, it is important to provide language engineering facilities for opportunistic reuse, extension, and customization of existing xDSMLs to ease the definition of new ones. Current approaches to language reuse either require to anticipate reuse, make use of advanced features that are not widely available in programming languages, or are not directly applicable to metamodel-based xDSMLs. In this paper, we propose a new language implementation pattern, named Revisitor, that enables independent extensibility of the syntax and semantics of metamodel-based xDSMLs with incremental compilation and without anticipation. We seamlessly implement our approach alongside the compilation chain of the Eclipse Modeling Framework, thereby demonstrating that it is directly and broadly applicable in various modeling environments. We show how it can be employed to incrementally extend both the syntax and semantics of the fUML language without requiring anticipation or re-compilation of existing code, and with acceptable performance penalty compared to classical handmade visitors.

Industry 4.0 is a vision of manufacturing in which smart, interconnected production systems optimize the complete value-added chain to reduce cost and time-to-market. At the core of Industry 4.0 is the smart factory of the future, whose successful deployment requires solving challenges from many domains. Model-based systems engineering (MBSE) is a key enabler for such complex systems of systems as can be seen by the increased number of related publications in key conferences and journals. This paper aims to characterize the state of the art of MBSE for the smart factory through a systematic mapping study on this topic. Adopting a detailed search strategy, 1466 papers were initially identified. Of these, 222 papers were selected and categorized using a particular classification scheme. Hence, we present the concerns addressed by the modeling community for Industry 4.0, how these are investigated, where these are published, and by whom. The resulting research landscape can help to understand, guide, and compare research in this field. In particular, this paper identifies the Industry 4.0 challenges addressed by the modeling community, but also the challenges that seem to be less investigated.

Abstract Authentication delegation is a major function of the modern web. Identity Providers (IdP) acquired a central role by providing this function to other web services. By knowing which web services or web applications access its service, an IdP can violate the enduser privacy by discovering information that the user did not want to share with its IdP. For instance, WebRTC introduces a new field of usage as authentication delegation happens during the call session establishment, between two users. As a result, an IdP can easily discover that Bob has a meeting with Alice. A second issue that increases the privacy violation is the lack of choice for the end-user to select its own IdP. Indeed, on many web-applications, the end-user can only select between a subset of IdPs, in most cases Facebook or Google. In this paper, we analyze this phenomena, in particular why the end-user cannot easily select its preferred IdP, though there exists standards in this field such as OpenID Connect and OAuth 2? To lead this analysis, we conduct three investigations. The first one is a field survey on OAuth 2 and OpenID Connect scope usage by web sites to understand if scopes requested by websites could allow for user defined IdPs. The second one tries to understand whether the problem comes from the OAuth 2 protocol or its implementations by IdP. The last one tries to understand if trust relations between websites and IdP could prevent the end user to select its own IdP. Finally, we sketch possible architecture for web browser based identity management, and report on the implementation of a prototype.

As cloud computing is being more and more used, datacenters play a large role in the overall energy consumption. We propose to tackle this problem, by continuously and autonomously optimizing the cloud datacenters energy efficiency. To this end, modeling the energy consumption for these infrastructures is crucial to drive the optimization process, anticipate the effects of aggressive optimization policies, and to determine precisely the gains brought with the planned optimization. Yet, it is very complex to model with accuracy the energy consumption of a physical device as it depends on several factors. Do we need a detailed and fine-grained energy model to perform good optimizations in the datacenter? Or is a simple and naive energy model good enough to propose viable energy-efficient optimizations? Through experiments, our results show that we don't get energy savings compared to classical bin-packing strategies but there are some gains inusing precise modeling: better utilization of the network and the VM migration processes.

The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.

Generally, compiler users apply different optimizations to generate efficient code with respect to non-functional properties such as energy consumption, execution time, etc. However, due to the huge number of optimizations provided by modern compilers, finding the best optimization sequence for a specific objective and a given program is more and more challenging. This paper proposes NOTICE, a component-based framework for non-functional testing of compilers through the monitoring of generated code in a controlled sand-boxing environment. We evaluate the effectiveness of our approach by verifying the optimizations performed by the GCC compiler. Our experimental results show that our approach is able to auto-tune compilers according to user requirements and construct optimizations that yield to better performance results than standard optimization levels. We also demonstrate that NOTICE can be used to automatically construct optimization levels that represent optimal trade-offs between multiple non-functional properties such as execution time and resource usage requirements.

In systems engineering, practitioners shall explore numerous architectural alternatives until choosing the most adequate variant. The decision-making process is most of the time a manual, time-consuming, and error-prone activity. The exploration and justification of architectural solutions is ad-hoc and mainly consists in a series of tries and errors on the modeling assets. In this paper, we report on an industrial case study in which we apply variability modeling techniques to automate the assessment and comparison of several candidate architectures (variants). We first describe how we can use a model-based approach such as the Common Variability Language (CVL) to specify the architectural variability. We show that the selection of an architectural variant is a multi-criteria decision problem in which there are numerous interactions (veto, favor, complementary) between criteria.

Open Cloud Computing Interface (OCCI) proposes one of the first widely accepted, community-based, open standards for managing any kinds of cloud resources. But as it is specified in natural language, OCCI is imprecise, ambiguous, incomplete, and needs a precise definition of its core concepts. Indeed, the OCCI Core Model has conceptual drawbacks: an imprecise semantics of its type classification system, a nonextensible data type system for OCCI attributes, a vague and limited extension concept and the absence of a configuration concept. To tackle these issues, this paper proposes a precise metamodel for OCCI. This metamodel defines rigourously the static semantics of the OCCI core concepts, of a precise type classification system, of an extensible data type system, and of both extension and configuration concepts. This metamodel is based on the Eclipse Modeling Framework (EMF), its structure is encoded with Ecore and its static semantics is rigourously defined with Object Constraint Language (OCL). As a consequence, this metamodel provides a concrete language to precisely define and exchange OCCI models. The validation of our metamodel is done on the first world-wide dataset of OCCI extensions already published in the literature, and addressing inter-cloud networking, infrastructure, platform, application, service management, cloud monitoring, and autonomic computing domains, respectively. This validation highlights simplicity, consistency, correctness, completeness, and usefulness of the proposed metamodel.

The proliferation of independently-developed and constantly-evolving domain-specific languages (DSLs) in many domains raises new challenges for the software language engineering community. Instead of starting the definition of new DSLs from scratch, language designers would benefit from the reuse of previously defined DSLs. While the support for engineering isolated DSLs is getting more and more mature, there is still little support in language workbenches for importing, assembling, and customizing legacy languages to form new ones. Melange is a new language workbench where new DSLs are built by assembling pieces of syntax and semantics. These pieces can be imported and subsequently extended, restricted, or customized to fit specific requirements. The demonstration will introduce the audience to the main features of Melange through the definition of an executable DSL for the design and execution of Internet of Things systems. Specifically, we will show how such a language can be obtained from the assembly of other popular languages while maintaining the compatibility with their tools and transformations.

Domain-Specific Languages (DSLs) are now developed for a wide variety of domains to address specific concerns in the development of complex systems. When engineering new DSLs, it is likely that previous efforts spent on the development of other languages could be leveraged, especially when their domains overlap. However, legacy DSLs may not fit exactly the end user requirements and thus require further extension, restriction, or specialization. While current language workbenches provide import mechanisms, they usually lack an explicit support for such customizations of imported artifacts. In this paper, we propose an approach for building DSLs by safely assembling and customizing legacy DSLs artifacts. This approach is based on typing relations that provide a reasoning layer for manipulating DSLs while ensuring type safety. On top of this reasoning layer, we provide an algebra of operators for extending, restricting, and assembling separate DSL artifacts. We implemented the typing relations and algebra into the Melange meta-language. We illustrate Melange through the modular definition of an executable modeling language for the Internet Of Things domain. We show how it eases the definition of new DSLs by maximizing the reuse of legacy artifacts without introducing issues in terms of performance, technical ecosystem compatibility, or generated code volume.

In systems engineering, the deployment of software components is error-prone since numerous safety and security rules have to be preserved. Furthermore, many deployments on different heterogeneous platforms are possible. In this paper we present a technological solution to assist industrial practitioners in producing a safe and secure solution out of numerous architectural variants. First, we introduce a pattern technology that provides correct-by-construction deployment models through the reuse of modeling artifacts organized in a catalog. Second, we develop a variability solution, connected to the pattern technology and based on an extension of the common variability language, for supporting the synthesis of model-based architectural variants. This paper describes a live demonstration of an industrial effort seeking to bridge the gap between variability modeling and model-based systems engineering practices. We illustrate the tooling support with an industrial case study (a secure radio platform).

Product Derivation is a key activity in Software Product Line Engineering. During this process, derivation operators modify or create core assets (e.g., model elements, source code instructions, components) by adding, removing or substituting them according to a given configuration. The result is a derived product that generally needs to conform to a programming or modeling language. Some operators lead to invalid products when applied to certain assets, some others do not; knowing this in advance can help to better use them, however this is challenging, specially if we consider assets expressed in extensive and complex languages such as Java. In this paper, we empirically answer the following question: which product line operators, applied to which program elements, can synthesize variants of programs that are incorrect, correct or perhaps even conforming to test suites? We implement source code transformations, based on the derivation operators of the Common Variability Language. We automatically synthesize more than 370,000 program variants from a set of 8 real large Java projects (up to 85,000 lines of code), obtaining an extensive panorama of the sanity of the operations.

Product Comparison Matrices (PCMs) form a rich source of data for comparing a set of related and competing products over numerous features. Despite their apparent simplicity, PCMs contain heterogeneous, ambiguous, uncontrolled and partial information that hinders their efficient exploitations. In this paper, we formalize PCMs through model-based automated techniques and develop additional tooling to support the edition and re-engineering of PCMs. 20 participants used our editor to evaluate the PCM metamodel and automated transformations. The results over 75 PCMs from Wikipedia show that (1) a significant proportion of the formalization of PCMs can be automated -- 93.11% of the 30061 cells are correctly formalized; (2) the rest of the formalization can be realized by using the editor and mapping cells to existing concepts of the metamodel. The automated approach opens avenues for engaging a community in the mining, re-engineering, edition, and exploitation of PCMs that now abound on the Internet.

3D printing is gaining more and more momentum to build customized product in a wide variety of fields. We conduct an exploratory study of Thingiverse, the most popular Website for sharing user-created 3D design files, in order to establish a possible connection with software product line (SPL) engineering. We report on the socio-technical aspects and current practices for modeling variability, implementing variability, configuring and deriving products, and reusing artefacts. We provide hints that SPL-alike techniques are practically used in 3D printing and thus relevant. Finally, we discuss why the customization in the 3D printing field represents a challenging playground for SPL engineering.

This paper discusses the merits and challenges of adopting software product line engineering (SPLE) as the main development process for an automotive Hall Effect sensor. This versatile component is integrated into a number of automotive applications with varying safety requirements (e.g., windshield wipers and brake pedals).

Modern component frameworks support continuous deployment and simultaneous execution of multiple software components on top of the same virtual machine. However, isolation between the various components is limited. A faulty version of any one of the software components can compromise the whole system by consuming all available resources. In this paper, we address the problem of efficiently identifying faulty software components running simultaneously in a single virtual machine. Current solutions that perform permanent and extensive monitoring to detect anomalies induce high overhead on the system, and can, by themselves, make the system unstable. In this paper we present an optimistic adaptive monitoring system to determine the faulty components of an application. Suspected components are finely instrumented for deeper analysis by the monitoring system, but only when required. Unsuspected components are left untouched and execute normally. Thus, we perform localized just-in-time monitoring that decreases the accumulated overhead of the monitoring system. We evaluate our approach against a state-of-the-art monitoring system and show that our technique correctly detects faulty components, while reducing overhead by an average of 80%.

Reusing software processes from a Software Process Line (SPL, i.e., a set of software processes that captures their commonalities and variabilities) and automating their execution is a way to reduce development costs. However, to our best knowledge no approach integrates both aspects. The difficulty is to automate the execution of a process whose variability is only partially resolved (i.e., a value is not set to each variable part of the process). Indeed, according to projects' constraints, it is possible to start the execution of a part of a process whose variability is resolved, while postponing the resolution of the variability of other parts of this process. In this paper, we propose a tool-supported approach that integrates both aspects. It consists of reusing processes from an SPL according to projects' requirements. The processes are bound to components that automate their execution. When the variability of a process to execute is not fully resolved, our approach consists of resolving this variability during the execution of this process. We illustrate this work on a family of processes for designing and implementing modeling languages. Our approach enables both the reuse of software processes and the automation of their execution, while enabling to resolve process variability during the execution.

Writing large Web applications is known to be difficult. One challenge comes from the fact that the application's logic is scattered into heterogeneous clients and servers, making it difficult to share code between both sides or to move code from one side to the other. Another challenge is performance: while Web applications rely on ever more code on the client-side, they may run on smart phones with limited hardware capabilities. These two challenges raise the following problem: how to benefit from high-level languages and libraries making code complexity easier to manage and abstracting over the clients and servers differences without trading this ease of engineering for performance? This article presents high-level abstractions defined as deep embedded DSLs in Scala that can generate efficient code leveraging the characteristics of both client and server environments. We compare performance on client-side against other candidate technologies and against hand written low-level JavaScript code. Though code written with our DSL has a high level of abstraction, our benchmark on a real world application reports that it runs as fast as hand tuned low-level JavaScript code.

Model-based Software Product Line (MSPL) engineering aims at deriving customized models corresponding to individual products of a family. MSPL approaches usually promote the joint use of a variability model, a base model expressed in a specific formalism, and a realization layer that maps variation points to model elements. The design space of an MSPL is extremely complex to manage for the engineer, since the number of variants may be exponential and the derived product models have to be conformant to numerous well-formedness and business rules. In this paper, the objective is to provide a way to generate MSPLs, called counterexamples, that can produce invalid product models despite a valid configuration in the variability model. We provide a systematic and automated process, based on the Common Variability Language (CVL), to randomly search the space of MSPLs for a specific formalism. We validate the effectiveness of this process for three formalisms at different scales (up to 247 metaclasses and 684 rules). We also explore and discuss how counterexamples could guide practitioners when customizing derivation engines, when implementing checking rules that prevent early incorrect CVL models, or simply when specifying an MSPL.

Variability on project requirements often implies variability on software processes. To manage such variability, Software Process Lines (SPLs) can be used to represent commonality (i.e., common practices) and variability (i.e., differences) of a set of related software processes. To this end, some Software Process Modeling Languages (SPMLs) natively integrate variability mechanisms. Nevertheless, such a coupling between the SPML and the variability mechanisms i) requires to interpret the requirements variability in terms of the processes variability, ii) limits the reuse of the requirements variability for other purposes (e.g., the development itself), and iii) is a barrier to the use of advances from the field of variability management. In this paper, we propose an approach to apply the Common Variability Language (CVL from the OMG consortium) for requirement variability modeling and its binding to the processes. This work is illustrated on a family of industrial Java development processes. Our approach enables the definition of an SPL and the automatic derivation of a process from this SPL according to the requirements of a given project. The variability is managed separately from the process model and benefits from existing tools coming from the process modeling community and CVL.

Modern software systems need to be continuously available under varying conditions. Their ability to dynamically adapt to their execution context is thus increasingly seen as a key to their success. Recently, many approaches were proposed to design and support the execution of Dynamically Adaptive Systems (DAS). However, the ability of a DAS to evolve is limited to the addition, update or removal of adaptation rules or reconfiguration scripts. These artifacts are very specific to the control loop managing such a DAS and runtime evolution of the DAS requirements may affect other parts of the DAS. In this paper, we argue to evolve all parts of the loop. We suggest leveraging recent advances in model-driven techniques to offer an approach that supports the evolution of both systems and their adaptation capabilities. The basic idea is to consider the control loop itself as an adaptive system.

Software Product Lines (SPLs) have evolved and gained attention as one of the most promising approaches for software reuse. Feature models are the main technique to represent domain variability in SPLs. However, there are other domain aspects, besides variability, which cannot be expressed in a feature model. Also, these diagrams were not designed to facilitate information retrieval, interoperability and inference. In contrast, ontologies seem to be the best solution to meet these requirements. Therefore, this work presents an approach for semantic enrichment of SPLs using ontologies. Our proposal provides methods to add domain information besides variability description, and a top-ontology that specifies generic concepts and relations in an SPL, working as a guide model for information addition. The proposed approach reuses the existing SPL feature model, adding semantic descriptions in a less intrusive way than modifying the feature model notation.

Integrating several legacy software systems together is commonly performed with multiple applications of the Adapter Design Pattern in OO languages such as Java. The integration is based on specifying bi-directional translations between pairs of APIs from different systems. Yet, manual development of wrappers to implement these translations is tedious, expensive and error-prone. In this paper, we explore how models, aspects and generative techniques can be used in conjunction to alleviate the implementation of multiple wrappers. Briefly the steps are, (1) the automatic reverse engineering of relevant concepts in APIs to high-level models; (2) the manual definition of mapping relationships between concepts in different models of APIs using an ad-hoc DSL; (3) the automatic generation of wrappers from these mapping specifications using AOP. This approach is weighted against manual development of wrappers using an industrial case study. Criteria are the relative code length and the increase of automation.

Security is a key-challenge for software engineering, especially when considering access control and software evolutions. No satisfying solution exists for maintaining the alignment of access control policies with the business logic. Current implementations of access control rely on the separation between the policy and the application code. In practice, this separation is not so strict and some rules are hard-coded within the application, making the evolution of the policy difficult. We propose a new methodology for implementing security-driven applications. From a policy defined by a security expert, we generate an architectural model, reflecting the access control policy. We leverage the advances in the [email protected] domain to keep this model synchronized with the running system. When the policy is updated, the architectural model is updated, which in turn reconfigures the running system. As a proof of concept, we apply the approach to the development of a library management system.

Since software systems need to be continuously available under varying conditions, their ability to evolve at runtime is increasingly seen as one key issue. Modern programming frameworks already provide support for dynamic adaptations. However the high-variability of features in Dynamic Adaptive Systems (DAS) introduces an explosion of possible runtime system configurations (often called modes) and mode transitions. Designing these configurations and their transitions is tedious and error-prone, making the system feature evolution difficult. While Aspect-Oriented Modeling (AOM) was introduced to improve the modularity of software, this paper presents how an AOM approach can be used to tame the combinatorial explosion of DAS modes. Using AOM techniques, we derive a wide range of modes by weaving aspects into an explicit model reflecting the runtime system. We use these generated modes to automatically adapt the system. We validate our approach on an adaptive middleware for home-automation currently deployed in Rennes metropolis.

The invasiveness of aspects is beneficial to modularize crosscutting concerns that require the modification of the data or control flow. However, it introduces subtle errors that are hard to locate and fix in case of evolution. In this paper we illustrate this issue by evolving a program implemented using aspects. Interaction issues, between aspects and the program, emerge from this evolution. We locate them through manual inspection and test execution. This tedious process motivates the need for an abstract specification of intended interactions. To tackle this issue, we propose a framework for specifying the types of invasiveness pattern that are allowed of forbidden in the program. We have also implemented a tool that automatically checks whether the specification is satisfied by the aspects.

In a highly dynamic environment, software systems requires a capacity of self-adaptation to fit the environment and the user needs evolution, which increases the software architecture complexity. Despite most current execution platforms include some facilities for handling dynamic adaptation, current design methodologies do not address this issue. One of the requirement for such a design process is to describe adaptation policies in a composable and qualitative fashion in order to cope with complexity. This paper introduces an approach for describing adaptation policies in a qualitative way while keeping the compositionality of adaptation policies. The basic example of a Web server is used to illustrate how to specify and to compose two adaptations policies which handle respectively the use of a cache and the deployment of new data sources.

Software architecture is a key concept in the design of a complex system. An architecture models the structure and behavior of the system, including the software elements and the relationships between them. While architectures were originally specified informally, recent years have seen the creation of a number of Architecture Description Languages (ADLs) [4]. ADLs are designed around the dimensions of composition and interaction, allowing the architect to introduce new concerns by constructing and combining increasingly complex elements