Setting up and configuring model-driven engineering (MDE) tools is not straightforward because the MDE tooling landscape is highly fragmented and because many MDE tools are research prototypes with limited documentation. This creates significant accidental complexity for learners of MDE, who have to overcome installation and configuration hurdles before they can even begin to focus on the core MDE concepts they should be learning. This is further complicated by the complexity of modern MDE tools, which can overwhelm new learners, making it difficult for them to work out what they should do next to achieve a given goal. To address these challenges, we have developed a web-based playground platform that enables learners to engage with MDE learning activities without the need to install anything. The playground metaphor allows teachers to expose only those functionalities directly required for the completion of a particular learning activity. We present the general architecture of the platform, our approach to the declarative integration of new MDE tools, and the way in which teachers can flexibly and declaratively define new MDE learning activities. We have used our platform in a range of different contexts, from live tutorials and 10-week university courses, to developing documentation webpages for MDE tools. We describe examples of such uses, showcasing the flexible configurability of the platform for different types of activities and contexts.

Validation and documentation of rationale are central to simulation studies. Most current approaches focus only on individual simulation artifacts—most typically simulation models—and their validity rather than their contribution to the overall simulation study. Approaches that aim to validate simulation studies as a whole either impose structured processes with the implicit assumption that this will ensure validity, or they rely on capturing provenance and rationale, most commonly in natural language, following accepted documentation guidelines. Inspired by dialectic approaches for developing mathematical proofs, we propose a vision of capturing validity and rationale information as a study unfolds through agent dialogues that also generate the overall simulation-study argument, as a novel approach to documenting and conducting simulation studies. We illustrate the key ideas in an example simulation study, highlight potential benefits of this novel approach, and identify key next steps towards making the vision a reality.

Abstract Modelling is an important activity in software development and it is essential that students learn the relevant skills. Modelling relies on dedicated tools and these can be complex to install, configure, and use—distracting students from learning key modelling concepts and creating accidental complexity for teachers. To address these challenges, we believe that modelling tools specifically aimed at use in teaching are required. Based on discussions at a working session organised at MODELS 2023 and the results from an internationally shared questionnaire, we report on requirements for such modelling tools for teaching. We also present examples of existing modelling tools for teaching and how they address some of the requirements identified.

Simulation is a key tool for researching complex system behaviour. Agent-based simulation has been applied across domains, such as biology, health, economics and urban sciences. However, engineering robust, efficient, maintainable, and reliable agent-based simulations is challenging. We present a vision for engineering agent simulations comprising a family of domain-specific modelling languages (DSMLs) that integrates core software engineering, validation and simulation experimentation. We relate the vision to examples of principled simulation, to show how the DSMLs would improve robustness, efficiency, and maintainability of simulations. Focusing on how to demonstrate the fitness for purpose of a simulator, the envisaged approach supports bi-directional transparency and traceability between the original domain understanding to the implementation, interpretation of results and evaluation of hypotheses.

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduceconsistency-preserving configuration operators(CPCOs)—genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.

Abstract Recently, there has been increased interest in combining model-driven engineering and search-based software engineering. Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the meta-heuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic multiplicity-preserving search operators for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.

Simulation is a key tool for researching complex system behaviour. Agent-based simulation has been applied across domains, such as biology, health, economics and urban sciences. However, engineering robust, efficient, maintainable, and reliable agent-based simulations is challenging. We present a vision for engineering agent simulations comprising a family of domain-specific modelling languages (DSMLs) that integrates core software engineering, validation and simulation experimentation. We relate the vision to examples of principled simulation, to show how the DSMLs would improve robustness, efficiency, and maintainability of simulations. Focusing on how to demonstrate the fitness for purpose of a simulator, the envisaged approach supports bi-directional transparency and traceability between the original domain understanding to the implementation, interpretation of results and evaluation of hypotheses.

In May 2019, a workshop on principled development of future agent-based simulations was held at Keele University. Participants spanned companies and academia, and a range of domains of interest, as well as participant career stages. This report summarizes the discussions and main outcomes from this workshop.

Recently there has been increased interest in combining the fields of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE). Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the meta-heuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic consistency preserving search operators (aCPSOs) for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies, and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.

Programming skills are an increasingly desirable asset across disciplines; however, learning to program continues to be difficult for many students. To improve pedagogy, we need to better understand the concepts that students find difficult and which have the biggest impact on their learning. Threshold-concept theory provides a potential lens on student learning, focusing on concepts that are troublesome and transformative. However, there is still a lack of consensus as to what the most relevant threshold concepts in programming are. The challenges involved are related to concept granularity and to evidencing some of the properties expected of threshold concepts. In this article, we report on a qualitative study aiming to address some of these concerns. The study involved focus groups with undergraduate students of different-year groups as well as professional software developers so as to gain insights into how perspectives on concepts change over time. Four concepts emerged from the data, where the majority of participants agreed on their troublesome nature—including abstract classes and data structures. Some of these concepts are considered transformative, too, but the evidence base is weaker. However, even though these concepts may not be considered transformative in the “big” sense of threshold concept theory, we argue the “soft” transformative effect of such concepts means they can provide important guidance for pedagogy and the design of programming courses. Further analysis of the data identified additional concepts that may hinder rather than help the learning of these threshold concepts, which we have called “accidental complexities.” We conclude the article with a critique of the use of threshold concepts as a lens for studying students’ learning of programming.

We consider a one-to-many persuasion setting, where a persuader presents arguments to a multi-party audience, aiming to convince them of some particular goal argument. The individual audience members each have differing personal knowledge, which they use, together with the arguments presented by the persuader, to determine whether they are convinced of the goal. The persuader must, therefore, carefully consider its strategy, i.e., which arguments to assert, in order to maximise the number of convinced audience members. Here, we use evolutionary search to find (near-)optimal strategies for the persuader. We implement our approach using search-based model engineering, which provides a natural and efficient encoding for such problems. We investigate the performance of our approach on a range of settings, considering different structures and sizes of argumentation frameworks (representing the underlying knowledge available to the persuader and audience members), and varying the size of audience and of the audience members' personal knowledge bases. We show that we can find effective strategies for problems with more than 200 arguments and more than 100 audience members. Further, we show that the approach supports multiple persuader objectives, finding persuader strategies that aim to minimise arguments to assert while still maximising the number of convinced audience members.

Elasticity refers to the auto-scaling ability of clouds towards optimally matching their resources to actual demand conditions. An important problem facing the infrastructure and service providers is how to optimise their resource configurations online, to elastically serve time-varying demands. Most scaling methodologies provide resource reconfiguration decisions to maintain quality properties under environment changes. However, issues related to the timeliness of such reconfiguration decisions are often neglected. In this paper, we present a methodology for online optimisation of cloud configurations. We first employ a search-based approach to extract near-optimal configurations considering conflicting performance and business quality attributes. Towards reducing the burden of time-consuming evaluations of configurations' quality, we develop surrogate models to predict their quality based on history observations. Finally, we evaluate our technique using Cloud Sim-based cloud simulation. Our experimental results show that the use of surrogates can produce high quality configurations with lead time of seconds and prediction error within 6%.

Infrastructure-less mobile ad-hoc networks enable the development of collaborative pervasive applications. Within such dynamic networks, collaboration between devices can be realised through service-orientation by abstracting device resources as services. Recently, a framework for QoS-aware service composition has been introduced which takes into account a spectrum of orchestration patterns, and enables compositions of a better QoS than traditional centralised orchestration approaches. In this paper, we focus on the automated exploration of trade-off compositions within the search space defined by this flexible composition model. For the studied problem, the evaluation of the fitness functions guiding the search process is computationally expensive because it either involves a high-fidelity simulation or actually requires calling the composite service. To overcome this limitation, we have developed efficient surrogate models for estimating the QoS metrics of a candidate solution during the search. Our experimental results show that the use of surrogates can produce solutions with good convergence and diversity properties at a much lower computational effort.

The requirement for elasticity involves the ability of cloud data centers to add or remove resources at a fine grain and with a lead time of seconds, closely matching resources to the actual demand conditions. Elasticity techniques can assist cloud stakeholders in regards to: (i) alleviating data center capital and operating costs, (ii) keeping cloud services continuously available, (iii) supporting market flexibility. To date, most infrastructure scaling methodologies can provide resource reconfiguration decisions to maintain quality properties under environment changes. However, issues related to the timeliness of reconfiguration decisions under dynamic changes are not adequately addressed. In this paper, we describe the PhD motivation, research questions and methodology towards developing cloud infrastructure and hosted services QoS optimisations under environment uncertainties.

As Cloud Computing becomes more predominant, large scale datacenters are subject to an increasing demand for efficiency and flexibility. However, growing infrastructure management complexity and maintenance costs are becoming a hindrance to the advancement of the Cloud vision. In this paper we discuss how existing datacenter resource management approaches fail to provide infrastructure elasticity and suggest a resources provisioning architecture to fill this gap. As a first step towards implementing our targets, we present a metamodel to describe the characteristics of the Cloud landscape, emphasising on a provider’s perspective. With this meta-model we intend to introduce new modelling concepts towards facilitating the selection of optimal reconfigurations in a timely fashion. 1

Service-oriented pervasive systems, composed of a large number of devices with heterogeneous capabilities where devices' resources are abstracted as software services, challenge the creation of high-quality composite applications. Resource heterogeneity, dynamic network connectivity, and a large number of highly distributed service providers complicate the process of creating applications with specific QoS requirements. Existing approaches to service composition control the QoS of an application solely by changing the set of participating concrete services which is not suitable for ad-hoc service-based systems characterised by high intermittent connectivity and resource heterogeneity. In this paper, we propose a flexible way of formulating composition configurations suitable for such service-based systems. Our formulation proposes the combined consideration of the following factors that affect the QoS of a composed service: (a) service selection, (b) orchestration partitioning, and (c) orchestrator node selection. We show that the proposed formulation enables the definition of service composition configurations with 49% lower response time, 28% lower network latency, 36% lower energy consumption, and 13% higher success ratio compared to those defined with the traditional approach.

In this work-in-progress paper, we present a new meta-model designed for the performance modeling of dynamic data center network infrastructures. Our approach models characteristic aspects of Cloud data centers which were not crucial in classical data centers. We present our meta-model and demonstrate its use for performance modeling and analysis through an example, including a transformation into OMNeT++ for performance simulation.

This workshop will explore the contributions that software research can make to the challenge of tackling climate change. Software is a critical enabling technology in nearly all aspects of climate change, from the computational models used by climate scientists to improve our understanding of the impact of human activities on earth systems, through to the information and control systems needed to build an effective carbon-neutral society. The intent of the workshop is to explore how software research can contribute to this challenge, to build a community of researchers interested in responding to the challenge, and to map out a research agenda.

In my research I try to define a framework which can be used to provide semantics for non-functional specifications of component-based systems. Some of the key questions driving my work are: What is the fundamental difference between functional and non-functional specifications? What formal apparatus is required to provide for this difference; What effects need to be taken into consideration when composing components with non-functional properties; What is the dependency between functional and nonfunctional specifications; What evaluation or analysis algorithms will be applied to a non-functional specification? What information must be extractable from the semantics of a nonfunctional specification?.

Component-based applications require runtime support to be able to guarantee non-functional properties. This paper proposes an architecture for a real-time-capable, component-based runtime environment, which allows to separate non-functional and functional concerns in component-based software development. The architecture is presented with particular focus on the real-time-non-real-time split of the runtime environment and the communication issues of respective component types and container parts.

We report on experience from teaching OO technology to undergraduate students. Before they can successfully tackle the projects they have to successfully shift to the OO paradigm, pick up a working knowledge of some OO language, learn and practice OOA and OOD, and get used to advanced ideas like patterns and frameworks. In order to relieve this heavy burden somewhat, we provided an object-oriented application framework as a common base for the projects. That way, the students are given an architecture which they have to adapt to their specific task instead of doing all the design by themselves. We also believe that this policy closely resembles the way beginners are integrated into on-going projects in practice. We describe the Java framework we used, the preparations for and the organization of the project course, educators' and students' experience and some ideas for developing this approach further.