Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Enterprise modelling (EM) refers to the systematic elicitation and documentation of organisational phenomena from several interrelated perspectives. Widely applied in information and computer sciences to engineer enterprises and information systems, it is rarely recognised as a research method in itself, much less outside these disciplines. This paper advocates that EM is a valid research method, within and outside its traditional application domains, highlighting its efficacy in eliciting and documenting organisational phenomena. Our study supports our claims with examples from a project investigating the application of EM as a research method to examine the intricate interplay between sustainability reporting and strategic management practices. Additionally, we have consulted experts in EM, who provided their opinions on the strengths and weaknesses of EM as a research method, and helped delineate a roadmap to foster its recognition and adoption. Throughout the paper, we emphasise the potential that EM brings to structure research and the lens that its modelling constructs provide to investigate organisational motivations, structures, processes, communications, information flow, technology, and their intricate interrelations.

This paper presents a model-driven AI-assisted approach for generating IT project management plan and scope documents, aiming to improve efficiency and quality in software development projects.Effective documentation in the early project phase is critical, yet often resource-intensive.The proposed solution consolidates best practices from widely used project management methodologies and standards to create a dynamic, adaptable framework for document generation.The study identifies key components and input data required for generating high-quality plans using model transformations and generative AI.A prototype supporting the solution is developed featuring a local processing engine, integrated with a large language model, a vector database, and an embedded model.

The interpretation of DNA variants enables personalized medicine through precise diagnosis and treatment selection. To address the challenges of manual interpretation, a wide range of automated tools has been created. This study evaluates these tools through a comprehensive analysis of their features, methodologies, and performance assessment against the interpretations of the ClinGen Expert Panel for 256 variants associated with cardiomyopathies, hereditary cancer, or monogenic diabetes. Although the tools demonstrated high accuracy for clearly pathogenic/benign variants, they showed significant limitations with variants of uncertain significance (VUS). Despite current advances in automation for variant interpretation, our findings show that expert oversight is still needed when using these tools in a clinical context, particularly for VUS interpretation.

Information and communication technology (ICT) brings about numerous advantages across various domains of our lives. However, alongside these benefits, there is a growing awareness of its potential negative ethical, social, and environmental impacts. Consequently, stakeholders ranging from conceptual modellers to policy makers often find themselves grappling with ethical considerations stemming from ICT engineering and usage. This paper presents a review of 10 ethical reasoning methods suitable for the ICT domain. We have employed a method engineering technique to author metamodels for the methods, which were subsequently subjected to validation by experts proficient in the respective methods. Following a situational method engineering approach, we have also characterised each ethical reasoning method and validated the characterisation with the experts. This has allowed us to develop a tool that helps select the method that is most suitable for a given ethical reasoning situation. Furthermore, we deliberate on the practical application of ethical reasoning methods within conceptual modelling contexts. We are confident that we have laid the groundwork for further research into ethical reasoning of ICT, with a specific emphasis on its role during conceptual modelling.

Alzheimer's disease is the most common type of dementia in the elderly. Nevertheless, there is an early onset form that is difficult to diagnose precisely. As the genetic component is the most critical factor in developing this disease, identifying relevant genetic variants is key to obtaining a more reliable and straightforward diagnosis. The information about these variants is stored in an extensive number of data sources, which must be carefully analyzed to select only the information with sufficient quality to be used in a clinical setting. This selection has become complex due to the increasing available genomic information. The SILE method was designed to systematize identifying relevant variants for a disease in this challenging context. However, several problems on how SILE identifies relevant variants were discovered when applying the method to the early onset form of Alzheimer's disease. More specifically, the method failed to address specific features of this disease such as its low incidence and familiar component. This paper proposes an improvement of the identification process defined by the SILE method to make it applicable to a further spectrum of diseases. Details of how the proposed solution has been applied are also reported. As a result of this improvement, a set of 29 variants has been identified (25 variants Accepted with a Limited Evidence and 4 Accepted with Moderate Evidence). This constitutes a valuable result that facilitates and reinforces the genetic diagnosis of the disease.

Model Driven Software Engineering (MDSE) and low-code/no-code software development tools promise to increase quality and productivity by modelling instead of coding software. One of the major advantages of modelling software is the increased possibility of involving diverse stakeholders since it removes the barrier of being IT experts to actively participate in software production processes. From an academic and industry point of view, the main question remains: What has been proposed to assist humans in software modelling tasks? In this paper, we systematically elucidate the state of the art in assistants for software modelling and their use in MDSE and low-code/no-code tools. We conducted a systematic mapping to review the state of the art and answer the following research questions: i) how is software modelling assisted? ii) what goals and limitations do existing modelling assistance proposals report? iii) which evaluation metrics and target users do existing modelling assistance proposals consider? For this purpose, we selected 58 proposals from 3.176 screened records and reviewed 17 MDSE and low-code/no-code tools from main market players published by the Gartner Magic Quadrant. We clustered existing proposals regarding their modelling assistance strategies, goals, limitations, evaluation metrics, and target users, both in research and practice. We found that both academic and industry proposals recognise the value of assisting software modelling. However, documentation about MDSE assistants’ limitations, evaluation metrics, and target users is scarce or non-existent. With the advent of artificial intelligence, we expect more assistants for MDSE and low-code/no-code software development will emerge, making imperative the need for well-founded frameworks for designing modelling assistants focused on addressing target users’ needs and advancing the state of the art.

Game Software Engineering (GSE) is a field that focuses on developing and maintaining the software part of video games. A key component of video game development is the utilization of game engines, with many engines using software models to capture various aspects of the game. A challenge that GSE faces is the localization of bugs, mainly when working with large and intricated software models. Additionally, the interaction between software models (i.e. bosses, enemies, or environmental elements) during gameplay is often a significant source of bugs. In response to this challenge, we propose a co-evolution approach for bug localization in the software models of video games, called CoEBA. The CoEBA approach leverages Search-Based Software Engineering (SBSE) techniques to locate bugs in software models while considering their interactions. We conducted an evaluation in which we applied our approach to a commercial video game, Kromaia. We compared our approach with a state-of-the-art baseline approach that relied on the bug localization approach used by Kromaia’s developers and a random search used as a sanity check. Our co-evolution approach outperforms the baseline approach in precision, recall, and F-measure. In addition, to provide evidence of the significance of our results, we conducted a statistical analysis. that shows significant differences in precision and recall values. The proposed approach, CoEBA, which considers the interaction between software models, can identify and locate bugs that other bug localization approaches may have overlooked.

Abstract The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling.

The use in the clinical practice of the vast amount of genomic data generated by current sequencing technologies constitutes a bottleneck for the progress of Precision Medicine (PM). Various problems inherent to the genomics domain (i.e., dispersion, heterogeneity, discrepancies, lack of standardization, and data quality issues) remain unsolved. In this paper, we present the Delfos platform, a conceptual model-based solution developed following a rigorous methodological and ontological background, whose main aim is to minimize the impact of these problems when transferring the research results to clinical practice. This paper presents the SILE method that provides methodological support for the Delfos platform, the Conceptual Schema of the Genome that provides a shared understanding of the domain, and the technological architecture behind the implementation of the platform. This paper also exemplifies the use of the Delfos platform through two use cases that involve the study of the DNA variants associated with the risk of developing Dilated Cardiomyopathies and Neuroblastoma.

The human genome complexity is captured by many signals, representing for instance DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data types and formats is used to record these signals. Conceptual models can support the description and explanation of the genome’s elaborate structure and behavior. Among others, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior, which is independent of data formats. The Genomic Conceptual Model (GCM) provides instead a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. In this research, we join the two approaches to achieve PoliViews, a comprehensive model that links (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. Their dynamic connection is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual: data records can be semantically described by high-level concepts exploiting their links and, in turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. PoliViews enables expressing queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. Here, we demonstrate the approach by linking two major genomic data types, namely DNA variation and gene expression. For each type, we consider different eminent data sources; we describe their mapping with the corresponding view in the concepts layer, enabling an intra-data-type integration. Then, leveraging on the connections available in the concepts layer, we show how the distinct data types can be interoperated, enabling an inter-data-type integration. The PoliViews approach is shown through several examples of biological interest and can be further extended to any kind of genomic information.

The usability requirements elicitation process is a difficult task that lacks methods to guide and help analysts, who are usually not experts at usability. This paper conducts an experiment with two replications to evaluate a method that elicits usability requirements based on structured interviews named UREM versus an unstructured method. The method consists of guided interviews by the analyst using decision trees. The tree is composed of questions and possible answers. Each question appears when there are different possible design alternatives, and each answer represents one of these alternatives. The tree also recommends the alternative that enhances the usability based on existing usability guidelines. We have conducted an experiment with two replications with 22 and 26 subjects playing two different roles in a within-subjects design. The analysts used a tree to guide the interview and elicit the requirements while the end users had to explain to the analyst the type of system to develop. During the interview, the analyst must design a paper prototype to be validated by the end user. For the analyst, the experiment measures the effectiveness of usability requirements elicitation, the effectiveness of the use of the usability guidelines, the efficiency of the elicitation process, and the satisfaction with the entire elicitation process. For the end user, the experiment measures the satisfaction with the designed prototype at the end of the interview. UREM yielded significantly better results for the effectiveness in the usability requirements elicitation process and for the effectiveness in the use of usability guidelines when compared to unstructured interviews. The use of UREM did not reduce the analysts’ efficiency and both analyst and end user remained the same satisfaction. Eliciting usability requirements is a difficult task if it is done with unstructured interviews and without usability recommendations.

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.

Software-centric organisations design a loosely coupled organisation structure around strategic objectives, replicating this design to their business processes and information systems. Nowadays, dealing with business strategy in a model-driven development context is a challenge since key concepts such as the organisation's structure and strategic ends and means have been mostly addressed at the enterprise architecture level for the strategic alignment of the whole organisation, and have not been included into MDD methods as a requirements source. To overcome this issue, researchers have designed the LiteStrat, a business strategy modelling method compliant with MDD for developing information systems. This article presents an empirical comparison of LiteStrat and with i*, one of the most used models for strategic alignment in an MDD context. The article contributes with a literature review on the experimental comparison of modelling languages, the design of a study for measuring and comparing the semantic quality of modelling languages, and empirical evidence of the LiteStrat and i* differences. The evaluation consists of a 2 × 2 factorial experiment recruiting 28 undergraduate subjects. Significant differences favouring LiteStrat were found for models' accuracy and completeness, while no differences in modeller's efficiency and satisfaction were detected. These results yield evidence of the suitability of LiteStrat for business strategy modelling in a model-driven context.

Search-Based Software Engineering (SBSE) offers solutions that efficiently explore large complex problem spaces. To obtain more favorable solutions, human participation in the search process is needed. However, humans cannot handle the same number of solutions as an algorithm. We propose the first hybrid fitness function that combines human effort with human simulations. Human effort refers to human participation for providing evaluations of candidate solutions during the search process, whereas human simulations refer to recreations of a scenario in a specific situation for automatically obtaining the evaluation of candidate solutions. We also propose three variants for the hybrid fitness function that vary in the distribution of human effort in order to study whether the variants influence the performance in terms of solution quality. Specifically, we leverage our hybrid fitness function to locate bugs in software models for the video games of game software engineering. Video games are a fertile domain for these hybrid functions because simulated players are naturally developed as part of the video games (e.g., bots in First-Person Shooters). Our evaluation is at the scale of industrial settings with a commercial video game (Play Station 4 and Steam) and 29 professional video game developers. Hybridizing the fitness function outperforms the results of the best baseline by 33.46% in F-measure. A focus group confirms the acceptance of the hybrid fitness function. Hybridizing the fitness function significantly improves the bug localization process by reducing the amount of tedious manual work and by minimizing the number of bugs that go unnoticed. Furthermore, the variant that obtains the best results is a counter-intuitive result that was under the radar of the interactive SBSE community. These results can help not only video game developers to locate bugs, but they can also inspire SBSE researchers to bring hybrid fitness functions to other software engineering tasks.

BACKGROUND: Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. RESULTS: In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the "ontological unpacking" method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. CONCLUSIONS: We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the "I" in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

BACKGROUND: All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. RESULTS: This work proposes a "systemist" perspective for creating a conceptual model of a life scientist's problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine. CONCLUSION: This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term "system," as a basic construct for conceptual modelling in life sciences.

BACKGROUND: Precision medicine is a promising approach that has revolutionized disease prevention and individualized treatment. The DELFOS oracle is a model-driven genomics platform that aids clinicians in identifying relevant variations that are associated with diseases. In its previous version, the DELFOS oracle did not consider the high degree of variability of genomics data over time. However, changes in genomics data have had a profound impact on clinicians' work and pose the need for changing past, present, and future clinical actions. Therefore, our objective in this work is to consider changes in genomics data over time in the DELFOS oracle. METHODS: Our objective has been achieved through three steps. First, we studied the characteristics of each database from which the DELFOS oracle extracts data. Second, we characterized which genomics concepts of the conceptual schema that supports the DELFOS oracle change over time. Third, we updated the DELFOS Oracle so that it can manage the temporal dimension. To validate our approach, we carried out a use case to illustrate how the new version of the DELFOS oracle handles the temporal dimension. RESULTS: Three events can change genomics data, namely, the addition of a new variation, the addition of a new link between a variation and a phenotype, and the update of a link between a variation and a phenotype. These events have been linked to the entities of the conceptual model that are affected by them. Finally, a new version of the DELFOS oracle that can deal with the temporal dimension has been implemented. CONCLUSION: Huge amounts of genomics data that is associated with diseases change over time, impacting patients' diagnosis and treatment. Including this information in the DELFOS oracle added an extra layer of complexity, but using a model-driven based approach mitigated the cost of implementing the needed changes. The new version handles the temporal dimension appropriately and eases clinicians' work.

The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggesting the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of “system” should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.

Abstract Increasingly, the model driven engineering (MDE) community is paying more attention to the techniques offered by the machine learning (ML) community. This has led to the application of ML techniques to MDE related tasks in hope of increasing the current benefits of MDE. Nevertheless, there is a lack of empirical experiments that evaluate the benefits that ML brings to MDE. In this work, we evaluate the benefits of empowering model engineers of model‐driven development (MDD) with an ML classifier. To do this, we tackled how to embed the ML classifier as part of the MDD. Then, this was evaluated using two different real industrial cases. Our results show that despite the ML part takes an extra effort, the use of the ML classifier pays off in terms of the quality results, the perceived usefulness, and intention to use.

With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context.

Abstract General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies what exists in reality. A widely used ontology in information systems, especially for conceptual modeling, is the BWW (Bunge–Wand–Weber), which is based on ideas of the philosopher and physicist Mario Bunge, as synthesized by Wand and Weber. The ontology was founded on an early subset of Bunge’s philosophy; however, many of Bunge’s ideas have evolved since then. An important question, therefore, is: do the more recent ideas expressed by Bunge call for a new ontology? In this paper, we conduct an analysis of Bunge’s earlier and more recent works to address this question. We present a new ontology based on Bunge’s later and broader works, which we refer to as Bunge’s Systemist Ontology (BSO) . We then compare BSO to the constructs of BWW. The comparison reveals both considerable overlap between BSO and BWW, as well as substantial differences. From this comparison and the initial exposition of BSO, we provide suggestions for further ontology studies and identify research questions that could provide a fruitful agenda for future scholarship in conceptual modeling and other areas of information technology.

Context: There is a lack of empirical evidence on the differences between model-driven development (MDD), where code is automatically derived from conceptual models, and traditional software development method, where code is manually written. In our previous work, we compared both methods in a baseline experiment concluding that quality of the software developed following MDD was significantly better only for more complex problems (with more function points). Quality was measured through test cases run on a functional system. Objective: This paper reports six replications of the baseline to study the impact of problem complexity on software quality in the context of MDD. Method: We conducted replications of two types: strict replications and object replications. Strict replications were similar to the baseline, whereas we used more complex experimental objects (problems) in the object replications. Results: MDD yields better quality independently of problem complexity with a moderate effect size. This effect is bigger for problems that are more complex. Conclusions: Thanks to the bigger size of the sample after aggregating replications, we discovered an effect that the baseline had not revealed due to the small sample size. The baseline results hold, which suggests that MDD yields better quality for more complex problems.

We live in an age where data acquisition is no longer a problem and the real challenge is how to determine which information is the right one to take important and sometimes difficult decisions. Infoxication (also known as Infobesity or Information Overload) is a term used to describe the difficulty of adapting to new situations and effectively making decisions when there is too much information to manage. With the advent of the Big Data, infoxication is affecting critical domains such as Health Sciences, where tough decisions for patient's health is being taken every day based on heterogeneous, unconnected and sometimes conflicting information. In order to understand the magnitude of the challenge, based on the information publicly available about the genetic causes of the disease and using data quality assessment techniques, we performed an exhaustive analysis of the DNA variations that have been associated to the risk of suffering migraine headache. The same analysis has been repeated 8 months after, and the results have allowed us to exemplify i) how fragile is the information in this domain, ii) the difficulty of finding repositories of contrasted and reliable data, and iii) the need to have information systems that, far from integrating and storing huge volumes of data, are able to support the decision-making process by providing mechanisms agile and flexible enough to be able to adapt to the changing user needs.

Welcome to EICS, the Engineering Interactive Computing Systems community, PACMHCI/EICS journal, and annual conference! In this short article, we introduce newcomers to the field and to our community with an overview of what EICS is and how it positions with respect to other venues in Human-Computer Interaction, such as CHI, UIST, and IUI, highlighting its legacy and paying homage to past scientific events from which EICS emerged. We also take this opportunity to enumerate and exemplify scientific contributions to the field of Engineering Interactive Computing Systems, which we hope to guide researchers and practitioners towards making their future PACMHCI/EICS submissions successful and impactful in the EICS community.

Business Process Model and Notation (BPMN) provides organizations with a standard that facilitates further compression of the business process. BPMN focuses on the functional processes, leaving the development of interfaces to one side. Thereby, interface design usually depends on the subjective experience of the analyst. This article aims to propose a new method to generate user interfaces from BPMN models and Class Diagrams. The proposed method is based on the identification of different rules and makes use of stereotypes to extend BPMN notation. The rules have been extracted from seven existing projects on the Bizagi repository. Specifically, the proposal is based on the extraction of rules for generating user interfaces based on three widely used patterns: sequence pattern, exclusive decision pattern and synchronization pattern. As a result of our proposal, eleven new stereotypes have been added to BPMN notation. These stereotypes allow generating interfaces based on business process models. For a better understanding, the proposed stereotypes have been applied to an illustrative example. The results show that this work is a “step forward to” the automatic code generation from models.

During the last two decades, data generated by Next Generation Sequencing Technologies have revolutionized our understanding of human biology and improved the study on how changes (variations) in the DNA are involved in the risk of suffering a certain disease. A huge amount of genomic data is publicly available and frequently used by the research community in order to extract meaningful and reliable gene-disease relationships. However, management of this exponential growth of data has become a challenge for biologists; under such a big data problem perspective, they are forced to delve into a lake of complex data spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality; but when data are used to solve a concrete problem only a small part of that “data lake” is really significant; this is what we call the “smart” data perspective. Using conceptual models and the principles of data quality management, adapted to the genomic domain, we propose a systematic approach to move from a big data to a smart data perspective. The aim of this approach is to populate an Information System with genomic data which must be accessible, informative and actionable enough to extract valuable knowledge.

While modern software development technologies enhance the capabilities of model-based/driven development, they introduce challenges for testers such as how to perform early testing at model level to ensure the quality of the model. In this context, we have developed an early testing technique supported by the CoSTest tool to validate requirements at model level. In this paper we describe an empirical evaluation of CoSTest with respect to its effectiveness in terms of its fault detection and test suite adequacy. This evaluation is carried out by model-level mutation testing using first order mutants (created by injection of a single fault) and high order mutants (containing more than one fault) with seven conceptual schemas (of different sizes) that represent the functionality of different software systems in different domains. Our findings show that the tests generated by CoSTest are effective at killing a large number of mutants. However, there are also some fault types (e.g. delete the references to a class attribute or an operation call in a constraint) that our test suites were not able to detect. CoSTest was more effective in terms of detecting fault types using high order mutants that first order mutants. Thus, CoSTest's effectiveness is affected by the mutant type tested.

Textual description of requirements is a specification technique that is widely used in industry, where time is key for success. How requirements are specified textually greatly depends on human factors. In order to study how requirements processing is affected by the level of detail in textual descriptions, this paper compares enriched textual requirements specifications with non-enriched ones. To do this, we have conducted an experiment in industry with 19 engineers of CAF (Construcciones y Auxiliares de Ferrocarril), which is a supplier of railway solutions. The experiment is a crossover design that analyzes efficiency, effectiveness, and perceived difficulty starting from a written specification of requirements that subjects must process in order to build software models. The results show that effectiveness and efficiency for enriched requirements are better, while non-enriched requirements are slightly more difficult to deal with. Therefore, even though enriched requirements require more time to be specified, the results are more successfully when using them.

Feature Location (FL) is a common task in the Software Engineering field, specially in maintenance and evolution of software products. The results of FL depend in a great manner in the style in which Feature Descriptions and software artifacts are written. Therefore, Natural Language Processing (NLP) techniques are used to process them. Through this paper, we analyze the influence of the most common NLP techniques over FL in Conceptual Models through Latent Semantic Indexing, and the influence of human participation when embedding domain knowledge in the process. We evaluated the techniques in a real-world industrial case study in the rolling stocks domain.

We present CoSTest, a tool that supports the validation of Conceptual Schemas by using testing. The tool implements techniques for transforming instantiations from a Requirements Model into test case implementations by supporting a Model-driven architecture.

Paul Johannesson; Mong Li Lee; Liddle, S.; Opdahl, A.; Pastor López, O. (2017). Special issue on conceptual modeling - 34th International Conference on Conceptual Modeling (ER 2015). Data & Knowledge Engineering. 109:1-2. doi:10.1016/j.datak.2017.03.001

[Background] Bug-fixing could be complex in industrial practice since thousands of products share features in their configuration. Despite the importance and complexity of bug-fixing, there is still a lack of empirical data about the difficulties found in industrial Software Product Lines (SPLs). [Aims] This paper aims to evaluate engineers' performance fixing errors and propagating the fixes to other configured products in the context of an industrial SPL. [Method] We designed and conducted an empirical study to collect data with regard to bug-fixing tasks within the context of a Induction Hob SPL in the BSH group, the largest manufacturer of home appliances in Europe. [Results] We found that effectiveness, efficiency and satisfaction got reached good values. Through interviews we also found difficulties related to unused features, cloning features unintentionally, detecting modified features, and propagating the fix when the source of the bug is the interaction between features. [Conclusions] The identified difficulties are relevant to know how to better apply SPLs in industry in the future.

Effective data management of clinical information is well-known to be a very complex task. The breast cancer context provides a good example of how challenging the problem is in Information Systems terms. In fact, it is one of the basic problems faced by clinicians who work in this area. However, this problem is even bigger when clinicians and biologists try to relate these data to biological data obtained from research studies about this disease. This paper shows how Conceptual Modeling practices provide reliable working environments where clinical and biological information are clearly linked. We focus on miRNAs expression studies in breast cancer patients as a representative study of biological data in this environment. Representing all these biological data using Conceptual Modeling techniques and connecting it with the clinical information properly modeled using the same technique, creates the appropriate background to develop an information system able to manage efficiently and analyze all this information.

This study presents the Architecture Specific Model (ASM) defined by the MoWebA approach to improve the development of web applications for different architectures. MoWebA is a model-driven approach to web applications development. The article presents a general overview of MoWebA, including the methodological aspects related to its modeling and transformation processes, the process of defining the ASM, and an example of an ASM model. We finally present a preliminary validation experience and its main results. The experience was structured according to the proposal of Runeson et al. for case studies.

[Context and motivation] The Model-Driven Engineering (MDE) paradigm remains an active niche in information systems engineering and research. Given their primary role, assuring the quality of models and modelling languages is crucial. In a previous literature review, we have confirmed that there exist different conceptions on model and modelling language quality. [Question/problem] Also, many approaches tackle a narrow perspective and practitioners often feel that such practices burden them with extra effort. A wider overview of the field is needed, taking into account the constraining and pressing conditions of industrial practice. [Principal ideas/results] This paper recalls the essence of, current trends and pitfalls of modelling language quality evaluation within MDE. Some of the current unsolved issues are related to a growing system complexity, evaluating the quality of a complementary set of languages, the need to bring model transformations in the picture, etc. [Contribution] Through this evaluation work, we define a research agenda that reveals the main challenges in modelling language quality evaluation in the context of MDE.

In Model-Driven Development (MDD), defects are managed at the level of conceptual models because the other artefacts are generated from them, such as more refined models, test cases and code. Although some studies have reported on defect types at model level, there still does not exist a clear and complete overview of the defect types that occur at the abstraction level. This paper presents a systematic mapping study to identify the model defect types reported in the literature and determine how they have been detected. Among the 282 articles published in software engineering area, 28 articles were selected for analysis. A total of 226 defects were identified, classified and their results analysed. For this, an appropriate defect classification scheme was built based on appropriate dimensions for models in an MDD context.

In the latter years, Next-Generation Sequencing (NGS) technologies cost decrease have increased the amount of information generated but also the complexity of managing this huge volume of data. Within this context, services and data integration are key when building new biological software or applying an existing one to a new environment. This paper proposes a service oriented framework factoring all the core services required to support NGS applications and provides an application example to multiple alignments using JABAWS to validate the proposed framework.

The Model-Driven Engineering (MDE) research must manage a diversity of conceptions despite the global truth about the use of conceptual models as one way for representing and managing the development of complex information systems. Due to this diversity of conceptions and the multiple MDE compliance interpretations, a pletora of definitions about quality in models are emerging, each one tackling specific dimensions involved in MDE projects. In order to explore a consensus about quality in models and model-driven contexts an identification of the previous proposals for quality in models is needed. The main contribution of this work is the identification of representative trends about quality definition in MDE, and therefore, exposing the implications of the multiple quality interpretations as consequence of the diversity in MDE compliance works.

Advanced interaction techniques are necessary to explore the potential of large data-volume systems. In this context, rich internet application patterns were defined, but usually reduced to the development of social web applications. However, other types of applications, such as data-analysis applications, require also advanced interaction solutions to assist users in making decisions and data-analysis. This paper identifies a set of problems emerged in the interaction between humans and data-analysis applications. We propose a set of guidelines for rich applications as a solution for these problems. As illustrative example of a real data-analysis environment, the paper focuses on a case study in the cultural heritage domain, highlighting the existing interaction problems and how they can be solved through the design guidelines proposed. The set of design guidelines allows to specify interfaces abstractly, creating a repository to solve interaction problems. These guidelines aim to serve as a basis for a future identification of new rich applications design patterns.

Next Generation Sequencing (NGS) technologies are becoming a commodity and, thus, genomic services delivered on line (e-genomics) is a growing market. This has led to the development of a plethora of tools traditionally covering only a small set of features of the process, though it's true than recently more complete tools have arisen. In such a scenario, integration between tools from different precedence becomes key to market evolution and consolidation. This paper proposes a service oriented framework factoring of the core services required to support NGS applications by identifying these services. This conforms a sound basis to provide a toolkit and a reference implementation, whose details are introduced in the work.

Usability is a quality attribute related to effectiveness, efficiency and satisfaction of the end-users when they interact with a system. There are sound usability design guidelines that aim to optimize the system usability. However, it is difficult to work with them if there is not a previous background in the Human-Computer Interaction area. Moreover, in the Software Engineering community, there are not specific methods to capture usability requirements. Usually, usability requirements are gathered with other non-functional requirements such as security or reliability, even though the goals of these non-functional requirements are not the same as the goals of usability. This problem arises when we aim to include the usability requirements capture in a Model Driven Development (MDD) process, where an effective capture technique with an unambiguous notation is needed to represent these requirements. In this paper, we propose a new method to capture usability requirements at the early stages of the software development process in such a way that non-experts in usability can use it. In a first step, experts in usability organize several interface design guidelines and usability guidelines in a tree structure. Next, these guidelines are shown to the analyst (non-expert in usability) through questions that she/he must ask the end-users. Answers to these questions mark the path through the tree structure. At the end of the process, if we gather all the end-user's answers, we have the usability requirements. These usability requirements could be transformed into a conceptual model of any existing MDD method by means of model to model transformations.

Enterprise systems need to evolve to be adapted with changes in their context. Model-driven reengineering frameworks have been developed in order to support post-delivery life cycle activities and software system maintenance (for instance, architecture-driven modernisation is an object management group initiative). However, existing proposals are general purpose and full support for an organisational reengineering framework is still needed. This paper presents a research effort aiming at designing and developing guidelines and tools for supporting model-driven organisational improvement. We propose to support model evolution by means of two artefacts: a pattern definition metamodel and an evolution metamodel. As a proof of concept, this paper applies the proposal to support business process evolution. We illustrate an instance of these metamodels by means of a laboratory-demo. We also discuss benefits and limitations of our proposal.

Models are key artifacts in Model-Driven Development (MDD) methods. To produce high-quality software by using MDD methods, quality assurance of models is of paramount importance. To evaluate the quality of models, defect detection is considered a suitable approach and is usually applied using reading techniques. However, these reading techniques have limitations and constraints, and new techniques are required to improve the efficiency at finding as many defects as possible. This article presents a case study that has been carried out to evaluate the use of a Functional Size Measurement (FSM) procedure in the detection of defects in models of an MDD environment. To do this, we compare the defects and the defect types found by an inspection group with the defects and the defect types found by the FSM procedure. The results indicate that the FSM is useful since it finds all the defects related to a specific defect type, it finds different defect types than an inspection group, and it finds defects related to the correctness and the consistency of the models.

This short paper summarizes and discusses the result of an iterative construction and evaluation of a checklist for writing and reading reports about experimental and observational research.

Model-driven development (MDD) is a promising paradigm for enterprise information systems. However, an open research challenge is to successfully integrate requirements practices into the MDD software lifecycle, and an open industrial challenge is to provide proper tool support for the complete software lifecycle. We propose to address these challenges by integrating Communication Analysis, (a communication-oriented requirements engineering method) and the OO-Method (a model-driven object-oriented software development method with full-fledged tool support and successful industrial practice). The OO-Method conceptual model offers four complementary views of the system under construction, which cover the structural, behavioural and presentation aspects. A requirements-based derivation of the Object Model (the structural view which consists of an extended UML Class Diagram) has been defined. This paper builds upon previous work by proposing a systematic technique for deriving the Dynamic Model (the behavioural view which consists of a collection of UML State Machines), taking as input the business process models. Some derivation guidelines are described and its usage is illustrated. The technique has been used in several lab demos that prove its feasibility. Moreover, the conceptual model that results from the structural and behavioural derivation can be used to automatically generate a fully-functional software application. As an additional benefit of the approach, the derivation allows tracing back and forth the elements of the requirements model, the conceptual model, and the final code.

Applying new learning methodologies in education, such as visual learning based on virtual reality and three-dimensional (3D) environments, is an important aspect in education, since it offers possibilities that can remarkably improve the current education system. Technological advances, along with the chance to create and represent the varying contents offered by information technologies, make the new learning methodologies the focus of attention in the future. Currently, 3D methodologies are only used in Computer Science to improve physical characteristics (virtual laboratories, virtual worlds, etc.), but they are not used to improve the internal mental processes by which human beings understand and retain abstract concepts. In these cases, the use of visual learning helps to clarify them. In Computer Science, particularly in Software measurement courses, the complexity of the concepts is possibly greater than in other courses because there is a lot of learning material that is based on abstract concepts that students find hard to recognize in the real world. In this paper, we present a visual environment that can be used to learn software measurement concepts like the IFPUG functional size measurement method. To validate the new learning model, an experiment was carried out.

Nowadays several Computer-Aided Software Engineering environments exploit Model-Driven Engineering (MDE) techniques in order to generate a single user interface for a given computing platform or multi-platform user interfaces for several computing platforms simultaneously. Therefore, there is a need to assess the usability of those generated user interfaces, either taken in isolation or compared to each other. This paper describes an MDE approach that generates multi-platform graphical user interfaces (e.g., desktop, web) that will be subject to an exploratory controlled experiment. The usability of user interfaces generated for the two mentioned platforms and used on multiple display devices (i.e., standard size, large, and small screens) has been examined in terms of satisfaction, effectiveness and efficiency. An experiment with a factorial design for repeated measures was conducted for 31 participants, i.e., postgraduate students and professors selected by convenience sampling. The data were collected with the help of questionnaires and forms and were analyzed using parametric and non-parametric tests such as ANOVA with repeated measures and Friedman's test, respectively. Efficiency was significantly better in large screens than in small ones as well as in the desktop platform rather than in the web platform, with a confidence level of 95%. The experiment also suggests that satisfaction tends to be better in standard size screens than in small ones. The results suggest that the tested MDE approach should incorporate enhancements in its multi-device/platform user interface generation process in order to improve its generated usability.

Models are key artifacts in Model-Driven Development (MDD) methods. To evaluate the quality of models, defect detection is considered to be a suitable approach, which is usually applied using reading techniques. However, new techniques are required in order to find as many defects as possible. This paper presents a case study to evaluate the usefulness of a Functional Size Measurement (FSM) procedure to detect defects in models of a MDD environment. The results indicate that the FSM is useful in finding all the defects that are related to a defect type as well as finding different defect types than an inspection team does.

It is widely accepted that the use of Conceptual Modeling techniques in modern Software Engineering leads to a more accurate description of the problem domain. The application of these techniques in the context of challenging domains as the human genome is a fascinating task. The relevant biological concepts should be properly addressed through the creation of the corresponding conceptual schema. This schema will improve the description of the global process followed from a DNA sequence to a fully functional protein. Once the conceptual model is established, the corresponding database is created. The database is intended to act as a unified repository of integrated information that will allow biologists to perform efficient recovery tasks.

This paper describes an empirical mapping study, which was designed to identify what aspects of software requirement specifications (SRS) are empirically evaluated, in which context, and by using which research method. On the basis of 46 identified and categorized primary studies, we found that understandability is the most commonly evaluated aspect of SRS, experiments are the most commonly used research method, and the academic environment is where most empirical evaluation takes place.

Software Engineering has a recurrent problem in relation to Business Process Modelling (BPM): there is no agreement with regards to business process modularity. We claim that this results from a lack of theoretical underpinnings on the matter. This paper goes deeply into this issue by unfolding the notion of modularity: modularisation has an engineering intention that depends on the field where it is applied, and it relies on information hiding and encapsulation mechanisms. Unity criteria provide guidance for encapsulation. An important contribution of the paper is to provide unity criteria for BPM. These criteria are mainly underpinned by systemic principles and Communication Theory. The resulting unity criteria allow to clearly differentiate between problem space and solution space in BPM. The argumentations are illustrated with explanatory examples and figures. Also, a historical review of unity criteria in Software Engineering and Requirements Engineering is offered.

A key requirement for MDD solutions is to have a modeling language that allows the correct representation of conceptual models. Nowadays, there are two options that are the most widely used for the definition of these modeling languages: 1) the specification of a domain-specific modeling language (DSML) or 2) the customization of UML. In practice, these two modeling alternatives are viewed as opposite solutions. However, since both alternatives provide benefits for the application of MDD solutions, in this paper, we present a proposal that uses UML profile extension mechanisms to interchange modeling information between DSML-based models and UML models. This proposal shows how these two modeling alternatives can be integrated in a unique MDD solution.

Requirements engineering (RE) is a relatively young discipline, and still many advances have been achieved during the last decades. In particular, numerous RE methods have been proposed. However, there is a growing concern for empirical validations that assess RE proposals and statements. This paper is related to the evaluation of the quality of functional requirements specifications, focusing on completeness and granularity. To do this, several concepts related to conceptual model quality are presented; these concepts lead to the definition of metrics that allow measuring certain aspects of a requirements model quality (e.g. degree of functional encapsulations completeness with respect to a reference model, number of functional fragmentation errors). A laboratory experiment with master students has been carried out, in order to compare (using the proposed metrics) two RE approaches; namely, Use Cases and Communication Analysis. Results indicate greater quality (in terms of completeness and granularity) when communication analysis guidelines are followed. Moreover, interesting issues arise from experimental results, which invite further research.

Traditionally, usability has been evaluated taking into account the user's satisfaction when interacting with the software system. However, in a Model-Driven Development (MDD) process, where conceptual models are the main resource for software system generation, the usability can potentially be evaluated at earlier stages. This work goes one step further proposing that certain usability attributes, specifically internal understandability attributes, can be measured from Conceptual Models. This work presents an empirical study carried out to evaluate the proposal. The goal of this study is to evaluate whether the value measured using our proposal is related to the understandability value perceived by the end user. From the analysis of the empirical results obtained, several weaknesses of the proposal are stated.

In this panel, we propose to figure out how requirements engineering and interaction design can be usefully combined. In particular, some people argue that scenarios/ use cases should be concrete, as in story-telling. Others argue for use of "essential" use cases as a methodological approach to interaction and user-interface design. Somewhat in the middle, it is argued that use cases should help acquiring the requirements in the first place. What should the practitioner believe and, in particular, do, in order to develop useful and usable software and systems? Software development and interaction design require different skills and different methods and are typically done by different people. Still, scenario-based design is proposed for several activities relevant for both tasks, such as requirements elicitation, software design, and interaction design. Symbolic modeling in this spirit is actually common to various fields. However, scenario-based approaches vary, especially with regard to their use, e.g., employing abstract use cases or integrating scenarios with functions and goals in a systematic design process. So, the key issue to be raised at the panel is how to combine different approaches, e.g., in scenario-based development, so that the interaction design as well as the development of the user interface and of the software internally result in an overall useful and useable system.

This paper introduces a method for specifying the functional requirements of pervasive systems. These requirements specifications can be mapped into PervML models, which enables (1) the early generation of prototypes to validate the requirements and (2) the definition of well defined transformations that provide traceability mechanisms for going from requirements to implementation and vice-versa

Great interest in model-driven approaches that are based on the automatic transformation of models has emerged. In this software development paradigm, the models and their transformations are specified at a high abstraction level supporting evolution, refinement and code generation. One of the benefits that these approaches try to achieve is improving the software quality through the reusability of well proven patterns. In order to achieve this goal, this paper presents an approach that defines patterns to transform use case models into interaction models. This approach uses roles to capture the linguistic abstractions on the use case text as well as to describe the corresponding interaction. The roles are defined as model elements to guarantee genericity. In this way, the transformation patterns are independent of a specific domain or a technological platform. The defined patterns have been validated. Some of the validation results are also presented in this paper.

The objective of this paper is to define a software production process which represents the correspondence between the primitive elements of a business model (represented in the framework i*) and the user interface of the software system. The representation of the user interface is compliant with the Unified Model Language (UML). We use a use case model as an intermediary between the business requirements and the application software. By doing this, we go a step further in the process of properly embedding early requirements engineering into the software production process, because organizational users can validate their requirements as early as possible. This is done through the validation of the user interfaces which are generated as a software representation of these requirements. These interfaces can also be reused for further refinement as a useful starting point in the software development process.

If we accept that computer representation of a conceptual model must be closer to the idea or the world the user has in mind, the conventional object-oriented methods fail in properly addressing the specification of semantics of presentation features within the conceptual model itself. To solve this problem, the conceptual model expressiveness must be enriched adding interface specification to capture such essential information. This information capture must be done preserving the semantics of the conceptual model as a whole.In this paper, a set of relevant interface patterns will be introduced in the conceptual modelling phase, preserving the homogeneity of the model. The enrichment is applied to OO Method, a software production OO method developed in the Information Systems and Computation Department at the Valencia University of Technology. Following the OO-Method approach a software product is automatically obtained from the conceptual model and the interface generation is obtained in a natural way.