Search

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.

Patient flow in emergency departments (EDs) is notoriously difficult to manage efficiently. While much of the attention has focused on the procedures, protocols and pathways in which patients receive their first hours of care, less attention has been paid to the relational factors that make it happen. Our study is the first, to our knowledge, to consider the role of interprofessional barriers, defined as suboptimal ways of working, as perceived by ED staff in patient flow management. Drawing on 19 interviews with hospital staff in an acute tertiary trauma center hospital in England, we established three flow-related types of interprofessional barriers: ED teamwork barriers, performance-driven coordination barriers, and referral-related collaborative barriers. Knotworking was recognized as a form of interactions and asset to teamworking, coordination, and collaboration. Identifying processes such as chasing, escalating, and advocating enabled our investigation to highlight a very complex set of interprofessional interactions, and signpost what the suboptimal practices of flow management are. Our analysis holds promise for hospitals beyond the National Health Service in England.

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.

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 introduce consistency-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., Comment: Accepted for publication in IEEE Transactions on Software Engineering (TSE). 16 pages, 10 figures; includes an appendix with 8 additional pages and 4 additional figures

Funding: This research was supported by the Engineering and Physical Sciences Research Council [grant number EP/P026842/1]. The subgraph isomorphism problem is to find a small “pattern” graph inside a larger “target” graph. There are excellent dedicated solvers for this problem, but they require substantial programming effort to handle the complex side constraints that often occur in practical applications of the problem; however, general purpose constraint solvers struggle on more difficult graph instances. We show how to combine the state of the art Glasgow Subgraph Solver with the Minion constraint programming solver to get a “subgraphs modulo theories” solver that is both performant and flexible. We also show how such an approach can be driven by the Essence high level modelling language, giving ease of modelling and prototyping to non-expert users. We give practical examples involving temporal graphs, typed graphs from software engineering, and costed subgraph isomorphism problems. Postprint

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.

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.

Where graphs are used for modelling and specifying systems, consistency is an important concern. To be a valid model of a system, the graph structure must satisfy a number of constraints. To date, consistency has primarily been viewed as a binary property: a graph either is or is not consistent with respect to a set of graph constraints. This has enabled the definition of notions such as constraint-preserving and constraint-guaranteeing graph transformations. Many practical applications—for example model repair or evolutionary search—implicitly assume a more graduated notion of consistency, but without an explicit formalisation only limited analysis of these applications is possible. In this paper, we introduce an explicit notion of consistency as a graduated property, depending on the number of constraint violations in a graph. We present two new characterisations of transformations (and transformation rules) enabling reasoning about the gradual introduction of consistency: while consistency-sustaining transformations do not decrease the consistency level, consistency-improving transformations strictly reduce the number of constraint violations. We show how these new definitions refine the existing concepts of constraint-preserving and constraint-guaranteeing transformations. To support a static analysis based on our characterisations, we present criteria for deciding which form of consistency ensuring transformations is induced by the application of a transformation rule. We illustrate our contributions in the context of an example from search-based model engineering.

Where graphs are used for modelling and specifying systems, consistency is an important concern. To be a valid model of a system, the graph structure must satisfy a number of constraints. To date, consistency has primarily been viewed as a binary property: a graph either is or is not consistent with respect to a set of graph constraints. This has enabled the definition of notions such as constraint-preserving and constraint-guaranteeing graph transformations. Many practical applications—for example model repair or evolutionary search—implicitly assume a more graduated notion of consistency, but without an explicit formalisation only limited analysis of these applications is possible. In this paper, we introduce an explicit notion of consistency as a graduated property, depending on the number of constraint violations in a graph. We present two new characterisations of transformations (and transformation rules) enabling reasoning about the gradual introduction of consistency: while consistency-sustaining transformations do not decrease the consistency level, consistency-improving transformations strictly reduce the number of constraint violations. We show how these new definitions refine the existing concepts of constraint-preserving and constraint-guaranteeing transformations. To support a static analysis based on our characterisations, we present criteria for deciding which form of consistency-ensuring transformations is induced by the application of a transformation rule. We validate our contributions in the context of an application in search-based model engineering.