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Context : Developing high-quality software requires skilled software professionals equipped with a set of basic and essential software engineering competencies (SEC). These competencies and the satisfaction levels derived from them change over a project's lifecycle, or as software professionals move from one project to another. Objective : Previous studies suggest a lack of means enabling SEC stakeholders to identify and assess competencies suitable for different projects. Additionally, previous research has mainly portrayed SEC to be static and overlooked their evolution over time and across projects. We investigate how we could effectively identify and match the competencies of software professionals necessary for different projects. Method : We follow a mixed-method approach to iteratively develop and evaluate a framework for identifying and managing SEC. In so doing, we use the results of an extensive literature review, focus group discussions with experts from academia and industry, and data collected through interviews with 138 individuals with a supervisory role in the software industry. Results : Drawing on the Kano model and Competency Framework for Software Engineers, we propose a Unified Competence Gate for Software Professionals (UComGSP), a framework for identifying and managing SEC. The UComGSP consists of 62 hard competencies, 63 soft competencies, and 25 essential SEC competencies. Additionally, we propose three stakeholders’ satisfaction levels for SEC assessment: basic, performance, and delighter. Furthermore, based on empirical observation, we report 27 competencies not mentioned in the reviewed literature; 11 of them are considered essential competencies. Conclusion : Competence development involves different stakeholders, including software professionals, educators, and the software industry. The UComGSP framework enables SEC stakeholders to (i) identify SE competencies, (ii) identify the essential SEC, and (iii) assess the satisfaction levels that can be derived from different competencies. Future research is needed to evaluate the effectiveness of the proposed framework across software development projects.

In software development life cycle (SDLC), the testing phase is important to test the functionalities of any software. In this phase, test cases are generated to test software functionalities. This paper presents an approach on how to detect and refactor code smells from the source codes of an Android application in order to reduce the redundancy in test case generation. Refactoring is one of the vital activities in software development and maintenance. Refactoring is a process of code alteration that aims to make structural modifications to the source code without altering any external functionality. These changes often improve software quality such as readability, execution time and maintainability. The proposed approach is then implemented and evaluated in order to determine its effectiveness in reducing the redundancy of test case generation. Keywords: Lazy class, Small method, Duplicate code smell, Code smell detection, Refactoring

Nurses are at the forefront of providing healthcare services to individuals of all age groups and with varying medical conditions. Aside from the critical knowledge and technical skills from nursing science, advancement in technology has assisted nurses in delivering quality nursing care by streamlining workflow processes and ensuring that data can easily be retrieved or modified. Electronic health records dramatically changed the landscape of the healthcare practice by providing an electronic means to store data and for healthcare professionals to retrieve and manipulate health information in a secured and collaborative environment. But with the nature of data being stored in the electronic health records, nurses still need to organize and process these data into relevant information, knowledge or wisdom so they can provide better holistic care to patients. This discussion paper details the role of content management systems in addressing nursing workflow by providing a mechanism for nurses to be developers themselves, and not just users or consumers of health innovative technologies. By using content management systems as platform for application development, nurses or other healthcare professionals, may be able to address problems with internal workflow without having to incur huge amounts in software development, or having to extensively learn programming languages. Keywords: Content management system, Electronic health records, Software, Health personnel, Nursing informatics, Programming languages, Workflow

The nature of hybrid technologies has been frequently interpreted with the concept of technology convergence. However, this concept tends to highlight only technical aspects of technology and market evolution. In order to provide a more comprehensive picture, the concept of sociotechnical alignment is explored here.\ud \ud The field of 3D bioprinting (the production of biological structures with automated, computer-controlled bioprinters) is focused on here. In the emergent global bioprinting market, companies have relied on three core technologies (tissue engineering, additive manufacturing, and software development) and continue to receive inputs from other technologies.\ud \ud On the biological side, bioprinting has benefited from new approaches such as the use of induced pluripotent stem cells. On the engineering side, it has been possible to use relatively cheap technologies such as open-source processing Arduino boards. On the software side, the proliferation of open source packages has strengthened the possibilities of bioprinting. The combination between these and other technology fringes involves a process of sociotechnical alignment whereby technical, scientific, and political issues are always at play.\ud \ud As a result, different companies have been able to realize different market strategies, having varied geographical reach. However, the first movements towards extensive globalization can also be noticed. In this way, the current diversity of the bioprinting market may be jeopardized in the years to come.

Energy modelling is critical for addressing challenges such as integrating variable renewable energy and addressing climate impacts. This paper describes the updated code management structure and code updates, the revised community forum and the outreach activities that have built a vibrant community of practice around OSeMOSYS. The code management structure has allowed code improvements to be incorporated into the model, the community forum provides users with a place to ask and answer questions, and the outreach activities connect members of the community. Overall, these three pillars show how a community of practice can be built around an open source tool and provides an example for other developers and users of open source software wanting to build a community of practice.

The current study uses knowledge from digital architecture, computer science, engineering informatics, and structural engineering to formulate an algorithmic framework for integrated Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) of Integrally-Attached Timber Plate (IATP) structures. The algorithm is designed to take the CAD 3D geometry of an IATP structure as input and automates the construction and analysis of the corresponding CAE model using a macroscopic element, which is an alternative to continuum Finite Element (FE) models. Each component of the macro model is assigned a unique tag that is linked to the relevant geometric and structural parameters. The CAE model integrity is maintained through the use of the common data model (CDM) concept and object-oriented programming. The relevant algorithms are implemented in Rhinoceros 3D using RhinoCommon, a .NET software development kit. Once the CAE macro model is generated, it is introduced to the OpenSees computational platform for structural analysis. The algorithmic framework is demonstrated using two case structures: a prefabricated timber beam with standard geometry and a free-form timber plate arch. The results are verified with measurements from physical experiments and FE models, where the time needed to convert thousands of CAD assemblies to the corresponding CAE models for response simulation is considerably reduced., Computers & Structures, 244, ISSN:0045-7949

GWpy is a Python software package that provides an intuitive, object-oriented interface through which to access, process, and visualise data from gravitational-wave detectors. GWpy provides a number of new utilities for studying data, as well as an improved user interface for a number of existing tools. The ease-of-use, along with extensive online documentation and examples, has resulted in widespread adoption of GWpy as a basis for Python software development in the international gravitational-wave community.

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Context: Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Objective: Although a myriad of research exists on software analytics as well as on Agile software development (ASD) practice on itself, there exists no systematic overview of the research done on ASD from a data analytics perspective. Therefore, the objective of this work is to make progress by linking ASD with Big Data analytics (BDA). Method: As the primary method to find relevant literature on the topic, we performed manual search and snowballing on papers published between 2011 and 2018. Results: In total, 65 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. Conclusions: As BDA and ASD are fast-developing areas, improving the productivity of software development teams is one of the most important objectives BDA is facing in the industry. This study provides scholars with information about the state of software analytics research and the current trends as well as applications in the business environment. Whereas, thanks to this literature review, practitioners should be able to understand better how to obtain actionable insights from their software artifacts and on which aspects of data analytics to focus when investing in such initiatives. This work is supported in part by the Catalan Agencia de Gestión deAyudas Universitarias y de Investigación (AGAUR) through the FI Ph.D. grant. The research is also partially supported by the Spanish Ministerio de Economía, Industria y Competitividad through the GENESIS project (grant TIN2016-79269-R).

Manufacturing companies are increasingly integrating agile approaches into their development processes. This is expected to improve customer integration, enhance responsiveness to changes in the development context and ultimately lead to improved process and product quality. However, since agile approaches mostly originate from the culture of software development and have been formulated on the basis of observations of successful software projects, new challenges arise in the application of these approaches in physical systems development. Often the approaches fail due to false expectations or lack of acceptance, which causes agility not to be deeply integrated into the processes. For this reason, this contribution presents an understanding of the current state of acceptance of agile working and the expected and perceived added value of using agile approaches in practice. Based on this understanding, future research will develop a systematic by means of which agile elements can be introduced into development processes in a way that is appropriate to the situation and needs, thereby increasing acceptance and perceived added value. Since it is empirical research, which analyses real-world processes, a survey was chosen as a suitable research method. 235 participants from different branches in the area of physical product development in Germany participated. The results were analyzed using usual statistical methods. An assumption that there are discrepancies between strategic and operational views on agile working could not be confirmed. However, optimization criteria in the area of acceptance of agile approaches were identified. The research contributed to the understanding of the current performance level in the field of agile development of physical systems. The identified potentials in the area of acceptance and perceived added value in agile work can now be measured specifically for each individual application and realized with suitable methods, what contributes to a sustainable integration of agile methods into the development processes.

Smart equipment as a cyber-physical system can be envisioned to support owner/operators with model-based assistance systems. In fluctuating process scenarios, which are expected within modular plants, frequent adaptation of the required simulation models to changing conditions will be necessary. Therefore, automated functional quality assessment for simulation models would provide a significant added value for the owner/operator. Currently, quality assessment of simulation models is dominated by the verification and validation methods developed by Sargent and co-authors ( Sargent and Balci, 2017 ). Although these methods provide a wide spectrum of assessment methods, they base on a rather narrow view on quality of simulation models. In addition, quality and quality advancement are typically not measured continuously. To overcome these obstacles, the authors discuss the applicability of central quality assurance methods from the field of software development to simulation model assessment. These are the principle of test-driven development and the application of quality models to measure quality and quality advancement. A framework and a workflow for test-driven development of simulation models are proposed and evaluated in a case study on a soft sensor for a fermentation process. It was found, that in principle, the framework provides transparent and continuous quality assessment in a semi-automated manner and lays the foundation for guidance strategies during model development.

As part of the evolution of the Space market in the last years – globally referred to as Space 2.0 - small companies are playing an increasingly relevant role in different aerospace projects. Business incubators established by European Space Agency (ESA) and similar entities are evidence of the need of moving initiatives to small companies characterized by greater flexibility to develop specific activities. Software is a key component in most aerospace projects, and the success of the initiatives and projects usually depends on the capability of developing reliable software following well-defined standards. But small entities face some difficulties when adopting software development standards that have been conceived thinking on larger organizations and big programs. The need of defining software development standards tailored to small companies and groups is a permanent subject of discussion not only in the aerospace field, and has led in recent years to the publication of the ISO/IEC 29110 series of systems and software engineering standards and guides, aimed to solve the issues that Very Small Entities (VSEs) () – settings having up to twenty-five people -, found with other standards like CMMI or SPICE. This paper discusses the tailoring defined by different aerospace organizations for VSEs in the aerospace industry, and presents a conceptual arrangement of the standard based on meta-modeling languages that allow the extension and full customization with the incorporation of specific software engineering requirements and practices from ECSS (European Cooperation for Space Standardization).

Coupled cluster Green's function (CCGF) approach has drawn much attention in recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. Here, we will present a brief review of the history of how the Green's function method evolved with the wavefunction, early and recent development of CCGF theory, and more recently scalable CCGF software development. We will highlight some of the recent applications of CCGF approach and propose some potential applications that would emerge in the near future.

The Leeds City Region (United Kingdom) has a significant digital skills gap, with employers struggling to recruit to positions in software development, software testing, business analysis, and project management. Local policy calls on universities and businesses to work together to address this gap. This case study considers initiatives undertaken by one university in Leeds to tackle this issue. It focuses on how to use employer and practitioner expertise in a multilayered approach to co-design both specialist programme content and general digital skills content across multiple programmes to engage students in the development of skills relevant to the local labour market. It also considers the challenges of contextualization, engagement, and the need to work with students to enable them to vision a digital future for themselves. It reflects on the positive outcomes of working in partnership with employers to respond to labour market issues and considers the position of students as stakeholders in such an agenda.

International audience; This article presents PAMELA, an annotation-based Java modeling framework. PAMELA provides a smooth integration between model and code and enables Java developers to handle software development both at conceptual level and at source-code level, without code transformation and/or generation, avoiding round-trip-related issues.

In the era of big data, a huge volume of heterogeneous healthcare and medical data are generated daily. These heterogeneous data, that are stored in diverse data formats, have to be integrated and stored in a standard way and format to perform suitable efficient and effective data analysis and visualization. These data, which are generated from different sources such as mobile devices, sensors, lab tests, clinical notes, social media, demographics data, diverse omics data, etc., can be structured, semistructured, or unstructured. These varieties of data structures require these big data to be stored not only in the standard relational databases but also in NoSQL databases. To provide effective data analysis, suitable classification and standardization of big data in medicine and healthcare are necessary, as well as excellent design and implementation of healthcare information systems. Regarding the security and privacy of the patient’s data, we suggest employing suitable data governance policies. Additionally, we suggest choosing of proper software development frameworks, tools, databases, in-database analytics, stream computing and data mining algorithms (supervised, unsupervised and semisupervised) to reveal valuable knowledge and insights from these healthcare and medical big data. Ultimately we propose the development of not only patient-oriented but also decision- and population-centric healthcare information systems.

Performance-based earthquake engineering (PBEE) has become an increasingly popular framework to design, retrofit and manage structures. This probabilistic framework integrates data from the seismic hazard, structural response, damage fragility and damage consequences to compute structural performance metrics; thereby facilitating an effective communication to building owners and stakeholders besides the engineering community. In support of PBEE, a Matlab-based computational platform/software is developed that implements state-of-the-art loss estimation and seismic lifecycle analysis methodologies. The software aims to (1) facilitate the relatively challenging PBEE computations by providing an intuitive graphical user-interface, a wide range of data visualization options and collective features that adapt to different users’ preferences; and (2) provide an open-source platform for code and software development to support the continuously advancing aspects of the PBEE framework.

This article presents PackageCargo. A modular open-source application developed using the Unity game engine to calculate, visualize, and save efficient packing patterns to instances of the Container Loading Problem (CLP). The packing patterns are obtained through approximate optimization algorithms (metaheuristics). Additionally, the proposed tool allows us to estimate cargo stability metrics through the implementation of mathematical models and verify the results of said models using a simulation environment built with the PhysX library. The goal of this application was to create a usable decision support system suitable for industrial purposes as well as a platform for academic research. It is offering a modifiable framework that can adapt to the necessities of its users, saving them software development time while continuing to extend PackageCargo through community contributions. The resulting application was compared with commercial software solutions. Furthermore, each module was tested using the most successful approaches found in literature as benchmarks. The packing module was compared against the top-performing algorithm published to date, obtaining similar results in similar computational times. The simulation module for cargo stability was benchmarked against high-performance simulation software, validating its accuracy and performance. Accordingly, PackageCargo was found to have a competitive feature set useful in both academic and commercial settings. As future work, it is proposed to combine the different modules to solve more sophisticated variants of the CLP, like the container loading problem constrained to weight distribution profiles.

This exploratory study investigates the relationship of plan-driven Stage-Gate and flexible Agile models with new product development performance through an original conceptualization that focuses on their underlying principles for managing uncertainty and the resulting changes. While Stage-Gate attempts to control uncertainty up-front to avoid later changes, Agile seeks to adapt to uncertainty and accommodate changes for a longer proportion of the development process. In addition, we examine the interaction effects of combining the two models. The analysis of survey data on 181 software developers shows that the adoption of Stage-Gate principles is negatively associated with speed and cost performance. For Agile, the use of sprints is positively related to new product quality, on-time and on-budget completion, while early and frequent user feedback would seem to prolong time-to-market. Finally, the results highlight a nuanced interaction between Stage-Gate and Agile, both positive and negative depending on the principles considered.

Design phase of any construction project involves several designers who exchange information with each other most often in an unstructured manner throughout the design phase. When these information exchanges happen to occur in cycles/loops, it is termed as design iteration. Iteration is an inherent and unavoidable aspect of any design phase which requires proper planning. Till date, very few researchers have explored the design iteration (“complexity”) in construction sector. Hence, the objective of this paper was to document and review the complexities of iteration during design phase of construction projects for efficient design planning. To achieve this objective, exhaustive literature review on design iteration was done for four sectors – construction, manufacturing, aerospace, and software development. In addition, semi-structured interviews and discussions were done with a few design experts to verify the different dimensions of iteration. Finally, a design iteration framework was presented in this study that facilitates successful planning. Keywords: Design iteration, Types of iteration, Causes and impact of iteration, Models of iteration, Execution strategies of iteration

Software defect prevention is an important way to reduce the defect introduction rate. As the primary cause of software defects, human error can be the key to understanding and preventing software defects. This paper proposes a defect prevention approach based on human error mechanisms: DPeHE. The approach includes both knowledge and regulation training in human error prevention. Knowledge training provides programmers with explicit knowledge on why programmers commit errors, what kinds of errors tend to be committed under different circumstances, and how these errors can be prevented. Regulation training further helps programmers to promote the awareness and ability to prevent human errors through practice. The practice is facilitated by a problem solving checklist and a root cause identification checklist. This paper provides a systematic framework that integrates knowledge across disciplines, e.g., cognitive science, software psychology and software engineering to defend against human errors in software development. Furthermore, we applied this approach in an international company at CMM Level 5 and a software development institution at CMM Level 1 in the Chinese Aviation Industry. The application cases show that the approach is feasible and effective in promoting developers’ ability to prevent software defects, independent of process maturity levels.

Collaborative development is a paradigm shift in software development. Loosely coupled developers coordinate their work via distributed versioning systems (SVN, Git, and others), code reviews and priority-led bug tracking systems. This development approach allows many different developers to input additional source code to the same source artifact. This article focuses on the lexical content of the source code produced in a collaborative environment. The lexical content is described as the ‘dictionary’ of the key terms contained within a source artifact. We posit that the lexical content of a Java class will increase as long as more developers add more content to the same class. We analyse the 100 top-ranked GitHub applications (at the time of the sampling) written in Java. Each of their classes is reduced to its lexical content, its size (in LOCs) recorded, as well as the number of different developers who contributed to its source code. Our results show that (i) the lexical content of Java classes is bounded in size, (ii) more developers make the size of the lexical content larger, and (iii) the lexical content of a system’s classes might increase with more developers, but depending on its application domain. The implications for practitioners are two-fold: (i) classes with a large set of lexical content should be split in multiple classes, to minimize the need for further maintenance; and (ii) classes developed by many developers should adhere to specific guidelines so that its lexical content does not increase boundlessly. We tested our results in a tailored case study and we confirmed our findings: larger-than-threshold class corpora tend to deteriorate the class cohesion.

SoftwareX 12, 100571 (2020). doi:10.1016/j.softx.2020.100571, Published by Elsevier, Amsterdam [u.a.]

Context: Exploiting software development related data from software-development intensive organizations to support tactical and strategic decision making is a challenge. Combining data-driven approaches with expert knowledge has been highlighted as a sensible approach for leading software-development intensive organizations to rightful decision-making improvements. However, most of the existing proposals lack of important aspects that hinders their industrial uptake such as: customization guidelines to fit the proposals to other contexts and/or automatic or semi-automatic data collection support for putting them forward in a real organization. As a result, existing proposals are rarely used in the industrial context. Objective: Support software-development intensive organizations with guidance and tools for exploiting software development related data and expert knowledge to improve their decision making. Method: We have developed a novel method called SESSI (Specification and Estimation of Software Strategic Indicators) that was articulated from industrial experiences with Nokia, Bittium, Softeam and iTTi in the context of Q-Rapids European project following a design science approach. As part of the industrial summative evaluation, we performed the first case study focused on the application of the method. Results: We detail the phases and steps of the SESSI method and illustrate its application in the development of ModelioNG, a software product of Modeliosoft development firm. Conclusion: The application of the SESSI method in the context of ModelioNG case study has provided us with useful feedback to improve the method and has evidenced that applying the method was feasible in this context. This work was supported by the Spanish Ministry of Economy and Competitiveness (project GENESIS, grant agreement TIN2016-79269- R), and by the European Union’s Horizon 2020 research and innovation program (Q-Rapids project, grant agreement N◦732253).

Model-based software development, in particular Model-Driven Engineering (MDE), has received a lot of attention during the last decade. More and more companies adopt MDE methods in software development. As a consequence, software engineers may need to deal with a large model base which becomes the main company asset. Moreover, there may be many configurations of the same model resulting in a large set of alternatives for the software engineers to take into account. This chapter presents the OptML Framework, which accepts various models defined in the Ecore MDE environment and computes the optimal models which satisfy user-defined constraints. Commonly used models in the MDE context such as class model, feature model, platform model, and process model are utilized in this chapter for illustration purposes. The examples of user-defined criteria are timeliness, energy consumption, and precision. To the best of our knowledge, this is the first generic MDE framework that is suitable for model optimization. The term generic here refers to a tool workbench architecture that is deliberately designed to extend the workbench with new features if necessary. The framework is fully implemented and tested for a selected set of models defined in the image processing domain.

This chapter looks at the standard tools that are both generic to software development and the tools that are specific to software and hardware integration for embedded, real-time systems. We’ll examine in-circuit emulators, logic analyzers, HW/SW coverification, and ROM emulation.

This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the codevelopments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging toward a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades. Furthermore, this chapter explores the shift from mathematical fundamentals and knowledge in software development toward skills in model validation, applied statistics, and integrated subject matter expertise. The review is interspersed with code examples to complement the theoretical foundations and illustrate model validation and machine learning explainability for science. The scope of this review includes various shallow machine learning methods, e.g., decision trees, random forests, support-vector machines, and Gaussian processes, as well as, deep neural networks, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. Regarding geoscience, the review has a bias toward geophysics but aims to strike a balance with geochemistry, geostatistics, and geology, however, excludes remote sensing, as this would exceed the scope. In general, I aim to provide context for the recent enthusiasm surrounding deep learning with respect to research, hardware, and software developments that enable successful application of shallow and deep machine learning in all disciplines of Earth science.

Large project overruns and overtime work have been reported in the software industry, resulting in additional expense for companies and personal issues for developers. The present work aims to provide an overview of studies related to time pressure in software engineering; specifically, existing definitions, possible causes, and metrics relevant to time pressure were collected, and a mapping of the studies to software processes and approaches was performed. Moreover, we synthesize results of existing quantitative studies on the effects of time pressure on software development, and offer practical takeaways for practitioners and researchers, based on empirical evidence. Our search strategy examined 5,414 sources, found through repository searches and snowballing. Applying inclusion and exclusion criteria resulted in the selection of 102 papers, which made relevant contributions related to time pressure in software engineering. The majority of high quality studies report increased productivity and decreased quality under time pressure. Frequent categories of studies focus on quality assurance, cost estimation, and process simulation. It appears that time pressure is usually caused by errors in cost estimation. The effect of time pressure is most often identified during software quality assurance. The majority of empirical studies report increased productivity under time pressure, while the most cost estimation and process simulation models assume that compressing the schedule increases the total needed hours. We also find evidence of the mediating effect of knowledge on the effects of time pressure, and that tight deadlines impact tasks with an algorithmic nature more severely. Future research should better contextualize quantitative studies to account for the existing conflicting results and to provide an understanding of situations when time pressure is either beneficial or harmful., 3 figures, currently in peer review

This chapter will explore the integration of several methodologies related to source code analysis and Requirements Engineering in a software development context. The chapter will review graph-based representations of source code, alongside applied type theory (for expressing programming languages’ type systems) and systematic accounts of foundational programming elements such as functions/procedures, function calls, and inter-procedure information flows. The new representational device described here involves a theory of “channels” which permits graph-based code models to be integrated with type theories and lambda calculi structured around modern programming paradigms. The proposed techniques support documentation and verification of procedural, data type, and holistic specifications—implementational assumptions on procedures and/or modeling assumptions on types, along with larger-scale inter-type relationships. This chapter will use real-world Cyber-Physical case studies to illustrate data models which call for advanced code-documentation techniques, insofar as Cyber-Physical software should prioritize safety and reliability. For concrete examples, an accompanying open-source dataset (at https://github.com/scignscape/ntxh ) demonstrates code libraries concretizing techniques outlined here.

We present an open-source library for coupling particle codes, such as molecular dynamics (MD) or the discrete element method (DEM), and grid based computational fluid dynamics (CFD). The application is focused on domain decomposition coupling, where a particle and continuum software model different parts of a single simulation domain with information exchange. This focus allows a simple library to be developed, with core mapping and communication handled by just four functions. Emphasis is on scaling on supercomputers, a tested cross-language library, deployment with containers and well-documented simple examples. Building on this core, a template is provided to facilitate the user development of common features for coupling, such as averaging routines and functions to apply constraint forces. The interface code for LAMMPS and OpenFOAM is provided to both include molecular detail in a continuum solver and model fluids flowing through a granular system. Two novel development features are highlighted which will be useful in the development of the next generation of multi-scale software: (i) The division of coupled code into a smaller blocks with testing over a range of processor topologies. (ii) The use of coupled mocking to facilitate coverage of various parts of the code and allow rapid prototyping. These two features aim to help users develop coupled models in a test-driven manner and focus on the physics of the problem instead of just software development. All presented code is open-source with detailed documentation on the dedicated website (cpl-library.org) permitting useful aspects to be evaluated and adopted in other projects. Program summary: Program Title: CPLLIBRARY Program Files doi: http://dx.doi.org/10.17632/9dh8w97d2x.1 Licensing provisions: GPLv3 Programming languages: Fortran/C/C++/Python External routines/libraries: Message Passing Interface (MPI) Nature of problem: Coupling of particle and continuum software to enable simulations not possible with either code alone. In particular, handling communication and interaction of computational fluid dynamics (CFD) software with either molecular dynamics (MD) or discrete element method (DEM) solvers on high performance computing (HPC) platforms. Solution method: A shared library with a minimal set of functions to enable coupling, together with an entire infrastructure to facilitate development of validated coupled software. This includes a minimal Python interface to encourage mock testing, libraries to help develop coupled tools along with pre-coupled examples including OpenFOAM, LAMMPS, Flowmol (Smith, 2014) and interactive plotting using wxPython and matplotlib. Unusual features: Minimal interface with simple setup. A CPL_Mocks framework to facilitate debugging and test driven development. Communication established between independent executables using MPI_Open_Port, reducing required changes to core source code. Coupled codes track the git repository with validation through continuous integration testing and deployment on DockerHub. All communication based on MPI_Cart and MPI_Graph so optimisation is possible through MPI implementation on supercomputers.

High Energy Physics Experiments (HEP experiments in the following) have been at least in the last 3 decades at the forefront of technology, in aspects like detector design and construction, number of collaborators, and complexity of data analyses. As uncommon in previous particle physics experiments, the computing and data handling aspects have not been marginal in their design and operations; the cost of the IT related components, from software development to storage systems and to distributed complex e-Infrastructures, has raised to a level which needs proper understanding and planning from the first moments in the lifetime of an experiment. In the following sections we will first try to explore the computing and software solutions developed and operated in the most relevant past and present experiments, with a focus on the technologies deployed; a technology tracking section is presented in order to pave the way to possible solutions for next decade experiments, and beyond. While the focus of this review is on offline computing model, the distinction is a shady one, and some experiments have already experienced contaminations between triggers selection and offline workflows; it is anticipated the trend will continue in the future.