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.

Abstract Participatory enterprise modeling (PEM) is presumed to have a positive impact on commitment, ownership feelings and further appraisals by domain experts with respect to the model. However, there has not been a lot of research into whether PEM actually has the desired effects. In this paper we report on an investigation of the effects of three settings with different levels of involving domain experts: an overall model was created (1) from four individual interviews, (2) from four individual models, or (3) in a joint meeting of domain and modeling consultants. The results show that the non-participatory interview setting led to less favorable appraisals, e.g., the possibility to participate and the value of the model were perceived as lower and the contribution of the modeling consultants was perceived as higher. Our findings should help practitioners in weighing possible benefits of participatory enterprise modeling against the organizational effort and monetary cost it involves.

Conceptual modelling can be used as a starting point for designing software systems and to better understand the domain knowledge related to requirements. This can act as a first step in Model-Driven Engineering (MDE) which is an approach to software development where code (or another artefact such as documentation or test cases) is automatically generated from a model. Assuming that the code or other artefacts are generated correctly from the designed conceptual model by the transformation engine, there is still no guarantee that the model correctly reflects the requirements for the system. This validation can take the form of model simulation, where the user can simulate real-world scenarios on the model. It has been found that students often struggle with modelling. Previous research has found that the use of a model simulation tool can improve student performance in terms of understanding the behavioural and structural aspects of a model and check the output for correctness. However, due to the exploratory nature of simulation tools, it is possible that some patterns of interacting with a model simulation tool are more typical of high achieving students than low achieving students. To this end, this research aims to analyse students' interaction behaviours in a model simulation tool in order to understand which approaches to model validation are most fruitful for good performance in an conceptual modelling course. This is done by collecting students' recorded user logs in a simulation tool and developing a process model using process mining. The results show that highperforming students show different interaction behaviours than average and low-performing students. Several recommendations for teaching conceptual modelling are derived from the identified interaction behaviours.

Software testing is an important part of the software development lifecycle. As it is a highly sought-after skill in the industry, it is not surprising that there has been a great deal of research into the teaching of software testing in higher education. Most of this research proposes or evaluates pedagogical approaches or software testing tools to assist teachers in educating the next generation of software engineers. These evaluations are often limited to measuring teachers’ opinions about the use of a novel pedagogical approach or an educational tool and students’ acceptance and performance in terms of desired software testing skills. While tools and pedagogical approaches address specific aspects of a course, to date, little attention has been paid to the opinions of the students about all the individual aspects of a software testing course. This paper aims to address this missing student perspective by taking a holistic view of software testing course designs. To address this gap, an exploratory study was performed by distributing a questionnaire to 103 students from ten different courses to gauge their opinions on a software testing course they are enrolled in. The results show that students generally have a positive perception of the different aspects of their software testing course. However, several areas for improvement were suggested based on the gathered data.

Software testing is an indispensable component of software development, yet it often receives insufficient attention. The lack of a robust testing culture within computer science and informatics curricula contributes to a shortage of testing expertise in the software industry. Addressing this problem at its root -education- is paramount. In this paper, we conduct a comprehensive mapping review of software testing courses, elucidating their core attributes and shedding light on prevalent subjects and instructional methodologies. We mapped 117 courses offered by Computer Science (and related) degrees in 49 academic institutions from four Western European countries, namely Belgium, Italy, Portugal and Spain. The testing subjects were mapped against the conceptual framework provided by the ISO/IEC/IEEE 29119 standard on software testing. Among the results, the study showed that dedicated software testing courses are offered by only 39% of the analysed universities, whereas the basics of software testing are taught in at least one course at every university. The analysis of the software testing topics highlights the gaps that need to be filled in order to better align the current academic offerings with the real industry needs.

It is important to properly test developed software because this may contribute to fewer bugs going unreported in deployed software. Often, little attention is spent on the topic of software testing in curricula, yielding graduate students without adequate preparation to deal with the quality standards required by the industry. This problem could be tackled by introducing bite-sized software testing education capsules that allow teachers to introduce software testing to their students in a less time-consuming manner and with a hands-on component that will facilitate learning. In order to design appropriate software testing educational tools, it is necessary to consider both the software testing needs of the industry and the cognitive models of students. This work-in-progress paper proposes an experimental design to gain an understanding of the cognitive strategies used by students during test case design based on real-life cases. Ultimately, the results of the experiment will be used to develop educational support for teaching software testing.

Abstract Conceptual modeling is a learning task essential to students of computer science, software engineering and related programs. Construed as a complex task, surprisingly little is known about the actual act of conceptual modeling, and about modeling difficulties learners experience. Combining complementary modes of observation of learners’ modeling processes, we study modeling difficulties encountered while performing a data modeling task. Using the concept of cognitive breakdown, we analyze audiovisual protocols of the individual modelers’ modeling processes, recordings of their interactions with the employed modeling software tool and survey data of modelers about their perception of encountered modeling difficulties. In an exploratory study and a follow-up study, we identify eight types of modeling difficulties related to modeling entity types, generalization hierarchies, relationship types, attributes and cardinalities. The identified types of modeling difficulties contribute to a better and more complete understanding of data modeling processes intended to inform design science research on modeling assistance for data modelers at different stages of their learning and mastering of conceptual data modeling.

Many designers and User Interface (UI) educators discuss principles to be followed when designing the functional aspects of a UI. However, many UI principles have been proposed, scattered in scientific papers and teaching books. Some principles are different, and many are somehow similar or overlapping with others. This makes it very difficult to comprehend where to pay attention to when designing a UI. In this paper, we perform a systematic literature review to first identify the most relevant authors in the domain of functional UI design principles. Focusing on the three most cited works of these authors, we extracted 257 principles. We next analyzed all these principles, unified their variants, and, considering their scientific influence, finally derived a shorter and core selection of 36 principles. This core selection provides educators and UI designers with a clear path to teach, evaluate, learn and improve the UI functional design.

Blended learning is gaining ground in contemporary education. However, studies on predictive learning analytics in the context of blended learning remain relatively scarce compared to Massive Open Online Courses (MOOCs), where such applications have gained a strong foothold. Data sets obtained from blended learning environments suffer from a high dimensionality and typically expose a limited number of instances, which makes predictive analysis a challenging task. In this work, we explore the log data of a master-level blended course to predict the students' grades based entirely on the data obtained from an online module (a small private online course), using and comparing logistic regression and random forest-based predictive models. The results of the analysis show that, despite the limited data, success vs. fail predictions can be made as early as in the middle of the course. This could be used in the future for timely interventions, both for failure prevention as well as for reinforcing positive learning behaviours of students.

Welcome to RCIS, the “Research Challenges in Information Science” community and its annual conference! In this short article, we introduce newcomers to this field and to its community with an overview of what RCIS is and how it positions with respect to other venues existing in the domain, like CAiSE, MODEL, and ER, highlighting its legacy. We also take this opportunity to enumerate and define the eight major RCIS areas of scientific contributions and exemplify them with some representative papers published during this 13thedition or before, which we hope to guide researchers and practitioners towards making their future RCIS submissions successful and impactful in the RCIS community.

User Interface (UI) design and software engineering complement each other to develop useful and usable interactive software systems. However, the body of knowledge for the development of an application and for the design of its UI are not always well integrated. The problem starts in the education of both subjects, which are normally taught independently of each other. Although an integrative teaching approach can significantly contribute to the development of better interactive software systems, there is a lack of concrete and proven approaches for such way of teaching. This paper presents a fully functional didactic tool for filling this gap. This tool provides the learner with feedback about how to develop an application and how to design a proper UI for it. Applying Model Driven Engineering principles, the tool automatically generates a working prototype of the interactive software system from its specification models, allowing the learner to try out the final application and validate the requirements. An experiment with novice developers demonstrates the advantages of this didactic tool.

When content consumers explicitly judge content positively, we consider them to be engaged. Unfortunately, explicit user evaluations are difficult to collect, as they require user effort. Therefore, we propose to use device interactions as implicit feedback to detect engagement. We assess the usefulness of swipe interactions on tablets for predicting engagement and make the comparison with using traditional features based on time spent. We gathered two unique datasets of more than 250,000 swipes, 100,000 unique article visits, and over 35,000 explicitly judged news articles by modifying two commonly used tablet apps of two newspapers. We tracked all device interactions of 407 experiment participants during one month of habitual news reading. We employed a behavioral metric as a proxy for engagement, because our analysis needed to be scalable to many users, and scanning behavior required us to allow users to indicate engagement quickly. We point out the importance of taking into account content ordering, report the most predictive features, zoom in on briefly read content and on the most frequently read articles. Our findings demonstrate that fine-grained tablet interactions are useful indicators of engagement for newsreaders on tablets. The best features successfully combine both time-based aspects and swipe interactions.

Task Models (TMs) that can adapt to context are a state-of-the-art executable modeling language that has proven to be successful in the automation and support of daily user tasks in pervasive systems. TMs are intuitive and easy to use to design such systems, however, they do not provide any verification technique for ensuring their correctness. To enable verification checking, we investigate and define mappings that can translate TMs to Context-Adaptive Petri Nets (CAPNs). CAPN is a recently proposed formalism to support Petri Nets (PNs) that take into account their execution context. Using PN as a base, CAPN provides powerful techniques for behavior simulation and verification. By applying the defined mappings, pervasive system's behavior can be intuitively represented using task models and then translated to their equivalent CAPNs. These CAPNs can be then used to perform an exhaustive checking of the represented behavior at design time in order to ensure a proper and safe system execution at runtime.

Providing individual and immediate feedback in educational situations is a critical factor for improving knowledge and skills acquisition. This is especially important for complex ill-structured learning tasks, i.e. tasks that are characterized by having multiple good solutions (ill-structured), allowing individual learners to follow different routes for achieving the final learning objectives, and having non-evident interactions between the different concepts in the problem domain. Conceptual modelling is an example of such complex learning task as it requires rigorous analytical skills and experience to externalize requirements into high-quality formal representations - conceptual models. These skills are very difficult to teach to novice modellers mainly due to the lack of tools that can continuously guide them in the learning process. In this talk, I will report about the use of automated feedback and simuation to guide the student's learning process for conceptual modelling. Furthermore, lessons from student modelling behaviour as observed from logging the modelling process of students will be presented. The findings include a set of typical modeling and validation patterns that can be used to improve teaching guidance for domain modeling courses. From a scientific viewpoint, the outcomes of the work can be inspirational outside of the area of domain modeling as learning event data is becoming readily available through virtual learning environments and other information systems.

Providing individual and immediate feedback to students is a critical factor for improving knowledge and skills acquisition in higher education. However, a growing number of students with very different heterogeneous profiles and unequal problem solving skills, as well as the lack of teaching resources, make it very challenging and sometimes even impossible to give immediate feedback at individual level. This paper presents a synthesis and progress of a long-term project that addresses this challenge in the context of conceptual modelling by developing SAiLE@CoMo, a smart and adaptive learning environment. By crafting innovative process analytics techniques and expert knowledge on feedback automation, SAiLE@CoMo can automatically provide personalized and immediate feedback to leaners.

Fraud is a social process that occurs over time. We introduce a new approach, called AFRAID, which utilizes active inference to better detect fraud in time-varying social networks. That is, classify nodes as fraudulent vs. non-fraudulent. In active inference on social networks, a set of unlabeled nodes is given to an oracle (in our case one or more fraud inspectors) to label. These labels are used to seed the inference process on previously trained classifier(s). The challenge in active inference is to select a small set of unlabeled nodes that would lead to the highest classification performance. Since fraud is highly adaptive and dynamic, selecting such nodes is even more challenging than in other settings. We apply our approach to a real-life fraud data set obtained from the Belgian Social Security Institution to detect social security fraud. In this setting, fraud is defined as the intentional failing of companies to pay tax contributions to the government. Thus, the social network is composed of companies and the links between companies indicate shared resources. Our approach, AFRAID, outperforms the approaches that do not utilize active inference by up to 15% in terms of precision.

The selection of classifiers which are profitable is becoming more and more important in real-life situations such as customer churn management campaigns in the telecommunication sector. In previous works, the expected maximum profit (EMP) metric has been proposed, which explicitly takes the cost of offer and the customer lifetime value (CLV) of retained customers into account. It thus permits the selection of the most profitable classifier, which better aligns with business requirements of end-users and stake holders. However, modelers are currently limited to applying this metric in the evaluation step. Hence, we expand on the previous body of work and introduce a classifier that incorporates the EMP metric in the construction of a classification model. Our technique, called ProfLogit, explicitly takes profit maximization concerns into account during the training step, rather than the evaluation step. The technique is based on a logistic regression model which is trained using a genetic algorithm (GA). By means of an empirical benchmark study applied to real-life data sets, we show that ProfLogit generates substantial profit improvements compared to the classic logistic model for many data sets. In addition, profit-maximized coefficient estimates differ considerably in magnitude from the maximum likelihood estimates.

To allow the distribution of control and visibility of cross-organizational process models and to increase availability and performance of the processes, a process model can be fragmented into logically different parts and distributed in the enterprise architecture. Fragmentation algorithms and execution environments that connect the fragmented process model parts together, recreating the original process execution semantics, have been proposed in earlier works. However, a critical challenge that is left open is the ability to independently change the control structure of a process fragment. This is not trivial as changing the control structure of a specific fragment could break the global process execution. The global process overview is also not available anymore because it is fragmented in the enactment environment and many independent control structure changes could have already been done. In this paper, we describe an approach based on state reconstruction, which enables checking the admissibility of changes made to the control structure of a process fragment in a fragmented event-based process enactment environment.

The complexity of model-driven engineering leads to a limited adoption of MDE in practice. In this paper we argue that MDE offers "low hanging fruit" if creating executable UML models is targeted rather than developing full-fledged information systems. This paper describes an environment for designing and validating conceptual business models using the model-driven architecture (MDA). The deliverable of the proposed modelling environment is an executable platform independent model (EPIM) that is further tested and validated through an MDA-based simulation feature. The proposed environment addresses a set of challenges associated with 1. shortcomings of the UML for being technically too complex for conceptual modelling goals as well as for being not precise enough for rapid prototyping; 2. difficulties of MDE adoption due to the large set of required skills to adopt the key MDA standards such as the UML and XMI for developing and maintaining a requirements prototyping tools. The paper aims to introduce the current work and identify the needs for future research.

When implementing (semi-)automatic business processes with services, engineers are facing two sources of variability. One source of variability are alternative refinements and decompositions of requirements. The other source of variability is that various (combinations of) services can be used to satisfy the same requirements. We suggest a method based on the use of a goal model and customizable services able to exploit these variabilities to design executable business process. This method improves the adaptability of the business process at runtime. We illustrate the contribution of our method with an example.

Business processes are designed to execute strategies that aim at achieving organisational goals. During the last decade, several methods have been proposed that prescribe the use of goal-oriented requirements engineering techniques for supporting different business process management activities, in particular business process modelling. The integration of goal modelling and business process modelling aims at increasing the alignment between business strategies and the processes with their supporting IT systems. This new research area, which the authors call Goal-Oriented Requirements Engineering for Business Processes (GORE-for-BP), is developing rapidly, but without a clear conceptualization of the focus and scope of the proposed GORE-for-BP methods. Furthermore, an overview is lacking of which methods exist and what their level of maturity is. This paper therefore presents a research review of the GORE-for-BP area, with the aim of identifying relevant methods and assessing their focus, scope, and maturity. This study used Systematic Literature Review and Method Meta-Modelling as research methods to identify and evaluate the state of the GORE-for-BP research area and to propose a research agenda for directing future research in the area. Nineteen methods were identified, which is an indication of an active research area. Although some similarities were found with respect to how goal models are transformed into business process models (or vice-versa), there is also considerable divergence in modelling languages used and the extent of coverage of typical requirements engineering and business process management lifecycle phases. Furthermore, the exploitation of requirements engineering techniques in the full business process management lifecycle is currently under researched. Also, the maturity of the methods found in terms of the formalisation of the transformation activity, the elaboration of method guidelines, and the extent to which methods are validated, can be further improved.

In this paper we propose a mathematical program able to optimize the product portfolio scope of a software product line and sketch both a development and a release planning. Our model is based on the description of customer needs in terms of goals. We show that this model can be instantiated in several contexts such as a market customization strategy or a mass-customization strategy. It can deal with Software Product Line development from scratch as well as starting from a legacy software base. We demonstrate its applicability with an example based on a case study.

The field of requirements engineering for business processes has grown during the last several years. As business processes are assumed to fulfil organizational goals, goal models could be transformed into business process models that specify how business processes fulfil the organizational goals. Although both the fields of Goal-Oriented RE (GORE) and Business Process Management (BPM) received a lot of attention from researchers, the methods to transform goal models into business process models still need further research. This scientific evaluation paper analyses current methods to identify the practical challenges that need to be addressed for an effective transformation of goal models into business process models. We illustrate the discovered challenges using the case study of Seven-Eleven Japan. The main challenges that were discovered are a lack of responsible role, insufficient concept mappings, informality of the transformation algorithm, no full support for organisational structure and a lack of inter-model consistency checks. Our expected contribution of discovering these challenges is to lower the barriers of transferring new goal-to-process methods to industry. As we consider i* constructs the most adequate in GORE for our purposes, we limit our study to transformations that start from i* goal models.

Web services standards are interesting to realize business-to-business integration (B2Bi). However, in the Web services domain too little attention has been paid to the importance of centralizing and decentralizing some data and process issues. This paper shows that these issues should get additional attention if Web services standards are to become more powerful and useful for real-life integration.

In a business process execution language (BPEL) process definition the sequence of exchanged messages typically originates from the sequence of business process activities and from the need of coordination of those activities across the participants of the process. As such business concerns (e.g. the sequence of business process steps) are of en mixed with technical aspects (e.g. the sequence of coordination messages). In this article we present an architecture to separate business and technical concerns, which results in a clearer overview of the high-level business process and improves the flexibility and maintainability of the orchestration architecture. The described architecture depends on existing Web service standards. Different eventing and coordination specifications are discussed. The ultimate architecture is mainly based on the WS-Brokered Notification and WS-Coordination framework specifications.

Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques.

In object-oriented conceptual modeling, the generalization/specialization hierarchy and the whole/part relationship are prevalent classification schemes for object types. This paper presents an object-oriented conceptual model where, in the end, object types are classified according to two relationships only. Existence dependency and generalization/specialization. Existence dependency captures some of the interesting semantics that are usually associated with the concept of aggregation (also called composition or Part Of relation), but in contrast with the latter concept, the semantics of existence dependency are very precise and its use clear cut. The key advantage of classifying object types according to existence dependency are the simplicity of the concept, its absolute unambiguity, and the fact that it enables to check conceptual schemes for semantic integrity and consistency. We will first define the notion of existence dependency and claim that it is always possible to classify objects according to this relationship, thus removing the necessity for the Part Of relation and other kinds of associations between object types. The second claim of this paper is that existence dependency is the key to semantic integrity checking to a level unknown to current object-oriented analysis methods. In other words: Existence dependency allows us to track and solve inconsistencies in an object-oriented conceptual schema.