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Despite potential benefits in Software Engineering, adoption of software modelling in industry is low. Technical issues such as tool support have gained significant research before, but individual guidance and training have received little attention. As a first step towards providing the necessary guidance in modelling, we conduct a systematic literature review to explore the current state of the art. We searched academic literature for guidance on model creation and selected 35 papers for full-text screening through three rounds of selection. We find research on model creation guidance to be fragmented, with inconsistent usage of terminology, and a lack of empirical validation or supporting evidence. We outline the different dimensions commonly used to provide guidance on software and system model creation. Additionally, we provide definitions of the three terms modelling method, style, and guideline as current literature lacks a well-defined distinction between them. These definitions can help distinguishing between important concepts and provide precise modelling guidance. [ABSTRACT FROM AUTHOR]

The authors discuss common control software maintenance tasks and tool capabilities based on existing research, and demonstrate the implementation of these capabilities in the 4diac IDE. However, there is limited empirical research on the usability and utility of model-based development environments for visual programming languages. The MODELS conference series is the premier venue for model-based software and systems engineering covering all aspects of modeling, from languages and methods to tools and applications. [Extracted from the article]

Provides an overview of selected papers that deals with different aspects of tool data integration techniques from a meta-modeling point of view, presented at the European Software Engineering Conference 2003 Workshop on Tool Integration in System Development, which took place on September 1-2, 2003 in Helsinki, Finland. Survey of model integration and transformation approaches; Suggested systematic approach for the development of the architecture of integrated development environments; List of sessions where the papers were presented.

In a world where Artificial Intelligence (AI) is pervasive, humans may feel threatened or at risk by giving up control to machines. In this context, ethicality becomes a major concern to prevent AI systems from being biased, making mistakes, or going rogue. Requirements Engineering (RE) is the research area that can exert a great impact in the development of ethical systems by design. However, proposing concepts, tools and techniques that support the incorporation of ethicality into the software development processes as explicit requirements remains a great challenge in the RE field. In this paper, we rely on Ontology-based Requirements Engineering (ObRE) as a method to elicit and analyze ethicality requirements ('Ethicality requirements' is adopted as a name for the class of requirements studied in this paper by analogy to other quality requirements studied in software engineering, such as usability, reliability, and portability, etc. The use of this term (as opposed to 'ethical requirements') highlights that they represent requirements for ethical systems, analogous to how 'trustworthiness requirements' represent requirements for trustworthy systems. To put simply: the predicates 'ethical' or 'trustworthy' are not meant to be predicated over the requirements themselves). ObRE applies ontological analysis to ontologically unpack terms and notions that are referred to in requirements elicitation. Moreover, this method instantiates the adopted ontology and uses it to guide the requirements analysis activity. In a previous paper, we presented a solution concerning two ethical principles, namely Beneficence and Non-maleficence. The present paper extends the previous work by targeting two other important ethicality principles, those of Explicability and Autonomy. For each of these new principles, we do ontological unpacking of the relevant concepts, and we present requirements elicitation and analysis guidelines, as well as examples in the context of a driverless car case. Furthermore, we validate our approach by analysing the requirements elicitation made for the driverless car case in contrast with a similar case, and by assessing our method's coverage w.r.t European Union guidelines for Trustworthy AI. [ABSTRACT FROM AUTHOR]

This theme section brings together the latest research at the intersection of artificial intelligence (AI) and model-driven engineering (MDE). Over the past years, we have witnessed a substantial rise of AI successfully applied to different domains, including software development and MDE. Dedicated events at the intersection of AI and MDE have been created, too, such as the MDE Intelligence workshop series co-located with the MODELS conference. This theme section covers research contributions integrating AI components into MDE approaches—increasing the current benefits of MDE processes and tools and pushing the limits of "classic" MDE with the goal to provide software and systems engineers with the right techniques to develop the next generation of highly complex model-based systems—and applications of MDE to the development of AI components. In total, nine submissions were accepted in the theme section after a thorough peer-reviewing process. [ABSTRACT FROM AUTHOR]

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research. [ABSTRACT FROM AUTHOR]

This theme section aims to disseminate the latest research results in the area of open environmental software systems modeling. Software-intensive systems, such as cyber-physical systems and self-adaptive systems, need to be able to constantly operate in open and dynamic environments, which requires them to continuously evolve, handle internal and external uncertainties, and so on. Traditional software engineering methodologies need to be extended for tackling these new challenges, and modeling is a promising technique to do that. This theme section contains two papers that tackle these issues, that were accepted after a thorough peer-reviewing process. Moreover, it contains an "Expert's Voice" contribution on the uncertainty interaction problem, which is a relevant issue in the systems considered in this theme section. [ABSTRACT FROM AUTHOR]

Low-code software development is heavily reliant on software reuse. The paper I Safe reuse in modeling language engineering using model subtyping with OCL constraints i by Artur Boronat focuses on low-code software development using Domain-Specific Languages (DSL) and Model-Driven Engineering (MDE). Software-intensive systems are becoming more complex, driven by the need to integrate across multiple aspects. [Extracted from the article]

This article provides a summary of the current state of the Software & Systems Modeling (SoSyM) journal in 2023. It highlights the publication of new papers and the addition of new editors to the journal. The retirement of two esteemed editors is also mentioned, with gratitude expressed for their contributions. The article includes statistics on the number of papers published, the impact factor of the journal, and the number of submissions and downloads. It announces the recipients of the journal's ten-year most influential paper awards and lists the papers presented at the MODELS conference. The article concludes by expressing appreciation to the reviewers for their contributions. Additionally, a separate document is a message of gratitude and recognition from the software and systems modeling community. The community expresses their appreciation to all the reviewers who have assisted the modeling community and announces the recipients of the Best Reviewers of 2023 award. The document also provides a list of all the reviewers who contributed their expertise to the journal over the past year. The content of the current issue is briefly mentioned, and the community wishes everyone a joyful New Year and encourages readers to explore the journal's archive. [Extracted from the article]

Models are the key tools humans use to manage complexity in description, development, and analysis. This applies to all scientific and engineering disciplines and in particular to the development of software and data-intensive systems. However, different methods and terminologies have become established in the individual disciplines, even in the sub-fields of Informatics, which raises the need for a comprehensive and cross-sectional analysis of the past, present, and future of modeling research. This paper aims to shed some light on how different modeling disciplines emerged and what characterizes them with a discussion of the potential toward a common modeling future. It focuses on the areas of software, data, and process modeling and reports on an analysis of the research approaches, goals, and visions pursued in each, as well as the methods used. This analysis is based on the results of a survey conducted in the communities concerned, on a bibliometric study, and on interviews with a prominent representative of each of these communities. The paper discusses the different viewpoints of the communities, their commonalities and differences, and identifies possible starting points for further collaboration. It further discusses current challenges for the communities in general and modeling as a research topic in particular and highlights visions for the future. [ABSTRACT FROM AUTHOR]

Business process modeling is a crucial aspect of domains such as Business Process Management and Software Engineering. The availability of various BPM languages in the market makes it challenging for process modelers to select the best-fit BPM language for a specific process modeling task. A decision model is necessary to systematically capture and make scattered knowledge on BPM languages available for reuse by process modelers and academics. This paper presents a decision model for the BPM language selection problem in research projects. The model contains mappings of 72 BPM features to 23 BPM languages. We validated and refined the decision model through 10 expert interviews with domain experts from various organizations. We evaluated the efficiency, validity, and generality of the decision model by conducting four case studies of academic research projects with their original researchers. The results confirmed that the decision model supports process modelers in the selection process by providing more insights into the decision process. Based on the empirical evidence from the case studies and domain expert feedback, we conclude that having the knowledge readily available in the decision model supports academics in making more informed decisions that align with their preferences and prioritized requirements. Furthermore, the captured knowledge provides a comprehensive overview of BPM languages, features, and quality characteristics that other researchers can employ to tackle future research challenges. Our observations indicate that BPMN is a commonly used modeling language for process modeling. Therefore, it is more sensible for academics to explain why they did not select BPMN than to discuss why they chose it for their research project(s). [ABSTRACT FROM AUTHOR]

Model-driven software engineering and specifically domain-specific languages have contributed to improve the quality of software and the efficiency in the development of software. However, the design and implementation of domain-specific languages requires still an enormous investment. Language workbenches are the most important tools in the field of software language engineering. The introduction of language workbenches has alleviated partly the development effort, but there are still a few major challenges that need to be tackled. This paper presents a personal perspective on the development of tools for language engineering and language workbenches in particular and future challenges to be tackled. [ABSTRACT FROM AUTHOR]

Blended modeling aims to improve the user experience of modeling activities by prioritizing the seamless interaction with models through multiple notations over the consistency of the models. Inconsistency tolerance, thus, becomes an important aspect in such settings. To understand the potential of current commercial and open-source modeling tools to support blended modeling, we have designed and carried out a systematic study. We identify challenges and opportunities in the tooling aspect of blended modeling. Specifically, we investigate the user-facing and implementation-related characteristics of existing modeling tools that already support multiple types of notations and map their support for other blended aspects, such as inconsistency tolerance, and elevated user experience. For the sake of completeness, we have conducted a multivocal study, encompassing an academic review, and grey literature review. We have reviewed nearly 5000 academic papers and nearly 1500 entries of grey literature. We have identified 133 candidate tools, and eventually selected 26 of them to represent the current spectrum of modeling tools. [ABSTRACT FROM AUTHOR]

Many powerful metamodeling platforms enabling model-driven software engineering (MDSE) exist, each with its strengths, weaknesses, functionalities, programming language(s), and developer community. Platform interoperability would enable users to exploit their mutual benefits. Such interoperability would allow the transformation of metamodels and models created in one platform into equivalent metamodels and models in other platforms. Language engineers could then freely choose the metamodeling platform without risking a lock-in effect. Two well-documented and publicly available metamodeling platforms are the eclipse modeling framework (EMF) and the modeling SDK for visual studio (MSDKVS). In this paper, we propose an M3-level-bridge (M3B) that establishes interoperability between EMF and MSDKVS on the abstract syntax level and on the graphical concrete syntax level. To establish such interoperability we (i) compare the two platforms, (ii) present a conceptual mapping between them, and (iii) implement a bidirectional transformation bridge including both the metamodel and model layer. We evaluate our approach by transforming a collection of publicly available metamodels and automatically generated or manually created models thereof. The transformation outcomes are then used to quantitatively and qualitatively evaluate the transformation's validity, executability, and expressiveness. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for the special section on SEFM 2020 and 2021 in the journal Software & Systems Modeling. The International Conference on Software Engineering and Formal Methods (SEFM) aims to bring together practitioners and researchers to advance the state of the art in formal methods and their application in the software industry. This special section includes selected papers from SEFM 2020 and 2021, which were held virtually during the COVID pandemic. Each article in this section has undergone rigorous peer review and has been revised and extended compared to its conference version. The selected papers cover topics such as embedded software portability and verification, nontermination inference, quantitative modeling and analysis of BDI agents, lazy model checking for recursive state machines, P-stable abstractions of hybrid systems, fairness and guarantees for extended bounded response LTL+P synthesis, counterexample classification, analyzing the impact of human errors on interactive service robotic scenarios, and active model learning of stochastic reactive systems. [Extracted from the article]

Industrial control applications are usually designed by domain experts instead of software engineers. These experts frequently use visual programming languages based on standards such as IEC 61131-3 and IEC 61499. The standards apply model-based engineering concepts to abstract from hardware and low-level communication. Developing industrial control software is challenging due to the fact that control systems are usually unique and need to be maintained for many years. The arising challenges, together with the growing complexity of control software, require very usable model-based development environments for visual programming languages. However, so far only little empirical research exists on the practical usefulness of such environments, i.e., their usability and utility. In this paper, we discuss common control software maintenance tasks and tool capabilities based on existing research and show the realization of these capabilities in the 4diac IDE. We performed a walkthrough of the demonstrated capabilities using the cognitive dimensions of notations framework from the field of human–computer interaction. We then improved the tool and conducted a user study involving ten industrial automation engineers, who used the 4diac IDE in a realistic control software maintenance scenario. Based on lessons learnt from this study, we adapted the 4diac IDE to better handle large graphical models. We evaluated these changes in a reassessment study with automation engineers from seven industrial enterprises. We derive general implications with respect to large-scale applications for developers of IDEs that we deem applicable in the context of (visual) model-based engineering tools. [ABSTRACT FROM AUTHOR]

Modeling is a key activity in conceptual design and system design. Through collaborative modeling, end-users, stakeholders, experts, and entrepreneurs are able to create a shared understanding of a system representation. While the Unified Modeling Language (UML) is one of the major conceptual modeling languages in object-oriented software engineering, more and more concerns arise from the modeling quality of UML and its tool-support. Among them, the limitation of the two-dimensional presentation of its notations and lack of natural collaborative modeling tools are reported to be significant. In this paper, we explore the potential of using virtual reality (VR) technology for collaborative UML software design by comparing it with classical collaborative software design using conventional devices (desktop PC/laptop). For this purpose, we have developed a VR modeling environment that offers a natural collaborative modeling experience for UML Class Diagrams. Based on a user study with 24 participants, we have compared collaborative VR modeling with conventional modeling with regard to efficiency, effectiveness, and user satisfaction. Results show that the use of VR has some disadvantages concerning efficiency and effectiveness, but the user's fun, the feeling of being in the same room with a remote collaborator, and the naturalness of collaboration were increased. [ABSTRACT FROM AUTHOR]

The OMG standard Systems Modeling Language (SysML) has been on the market for about thirteen years. This standard is an extended subset of UML providing a graphical modeling language for designing complex systems by considering software as well as hardware parts. Over the period of thirteen years, many publications have covered various aspects of SysML in different research fields. The aim of this paper is to conduct a systematic mapping study about SysML to identify the different categories of papers, (i) to get an overview of existing research topics and groups, (ii) to identify whether there are any publication trends, and (iii) to uncover possible missing links. We followed the guidelines for conducting a systematic mapping study by Petersen et al. (Inf Softw Technol 64:1–18, 2015) to analyze SysML publications from 2005 to 2017. Our analysis revealed the following main findings: (i) there is a growing scientific interest in SysML in the last years particularly in the research field of Software Engineering, (ii) SysML is mostly used in the design or validation phase, rather than in the implementation phase, (iii) the most commonly used diagram types are the SysML-specific requirement diagram, parametric diagram, and block diagram, together with the activity diagram and state machine diagram known from UML, (iv) SysML is a specific UML profile mostly used in systems engineering; however, the language has to be customized to accommodate domain-specific aspects, (v) related to collaborations for SysML research over the world, there are more individual research groups than large international networks. This study provides a solid basis for classifying existing approaches for SysML. Researchers can use our results (i) for identifying open research issues, (ii) for a better understanding of the state of the art, and (iii) as a reference for finding specific approaches about SysML. [ABSTRACT FROM AUTHOR]

Mobile robots operate in various environments (e.g. aquatic, aerial, or terrestrial), they come in many diverse shapes and they are increasingly becoming parts of our lives. The successful engineering of mobile robotics systems demands the interdisciplinary collaboration of experts from different domains, such as mechanical and electrical engineering, artificial intelligence, and systems engineering. Research and industry have tried to tackle this heterogeneity by proposing a multitude of model-driven solutions to engineer the software of mobile robotics systems. However, there is no systematic study of the state of the art in model-driven engineering (MDE) for mobile robotics systems that could guide research or practitioners in finding model-driven solutions and tools to efficiently engineer mobile robotics systems. The paper is contributing to this direction by providing a map of software engineering research in MDE that investigates (1) which types of robots are supported by existing MDE approaches, (2) the types and characteristics of MRSs that are engineered using MDE approaches, (3) a description of how MDE approaches support the engineering of MRSs, (4) how existing MDE approaches are validated, and (5) how tools support existing MDE approaches. We also provide a replication package to assess, extend, and/or replicate the study. The results of this work and the highlighted challenges can guide researchers and practitioners from robotics and software engineering through the research landscape. [ABSTRACT FROM AUTHOR]

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

In this paper, we present a novel technique to automatically synthesize consistent, diverse and structurally realistic domain-specific graph models. A graph model is (1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints of the domain; (2) it is diverse if local neighborhoods of nodes are highly different; and (1) it is structurally realistic if a synthetic graph is at a close distance to a representative real model according to various graph metrics used in network science, databases or software engineering. Our approach grows models by model extension operators using a hill-climbing strategy in a way that (A) ensures that there are no constraint violation in the models (for consistency reasons), while (B) more realistic candidates are selected to minimize a target metric value (wrt. the representative real model). We evaluate the effectiveness of the approach for generating realistic models using multiple metrics for guidance heuristics and compared to other model generators in the context of three case studies with a large set of real human models. We also highlight that our technique is able to generate a diverse set of models, which is a requirement in many testing scenarios. [ABSTRACT FROM AUTHOR]

This paper follows a paper, 'UML in Practice' presented at ICSE 2013. It summarizes and reflects on the discussion and additional investigation that arose from 'UML in Practice.' The paper provides a condensed recap of 'UML in Practice' findings, explains what data were collected from which sources to inform this paper, and describes how the data were analyzed. It reports on the discussion that has arisen, summarizing responses from industry practitioners, academics teaching software engineering, and the UML community, and considers how those responses reflect on the original observations. The responses to 'UML in Practice' divide (crudely) between two perspectives: (1) the observations made are familiar and unsurprizing, and match personal experience ('No shit'); or (2) the observations threaten long-held beliefs about UML use, and in particular about the status of UML as the de facto standard of software engineering, implying a need to change personal practice ('Oh, shit!'). [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special section in the journal "Software & Systems Modeling." The special section features revised and extended versions of selected papers from the 20th International Conference on Software Engineering and Formal Methods (SEFM'22), which was held in Berlin, Germany in September 2022. The SEFM conference series aims to advance the state of the art in formal methods and their integration into practical software engineering methods and tools. The special section includes papers on topics such as user-centric analysis, automated generation of finite automata, formal specifications of smart contracts, distributed verification of programs, and abstraction-based verification of neural networks. The authors express their gratitude to the authors and reviewers for their contributions and thank Springer for their support in publishing the special section. [Extracted from the article]

The development process of on-board software applications can benefit from model-driven engineering techniques. Model validation and model transformations can be applied to drive the activities of specification, requirements definition, and system-level validation and verification according to the space software engineering standards ECSS-E-ST-40 and ECSS-Q-ST-80. This paper presents a model-driven approach to completing these activities by avoiding inconsistencies between the documents that support them and providing the ability to automatically generate the system-level validation tests that are run on the Ground Support Equipment and the matrices required to complete the software verification. A demonstrator of the approach has been built using as a proof of concept a subset of the functionality of the software of the control unit of the Energetic Particle Detector instrument on-board Solar Orbiter. [ABSTRACT FROM AUTHOR]

In recent years, a large number of secure voting protocols have been proposed in the literature. Often these protocols contain flaws, but because they are complex protocols, rigorous formal analysis has proven hard to come by. Rivest's ThreeBallot and Vote/Anti-Vote/Vote (VAV) voting systems are important because they aim to provide security (voter anonymity and voter verifiability) without requiring cryptography. In this paper, we construct CSP models of ThreeBallot and VAV, and use them to produce the first automated formal analysis of their anonymity properties. Along the way, we discover that one of the crucial assumptions under which ThreeBallot and VAV (and many other voting systems) operate-the short ballot assumption-is highly ambiguous in the literature. We give various plausible precise interpretations and discover that in each case, the interpretation either is unrealistically strong or else fails to ensure anonymity. We give a revised version of the short ballot assumption for ThreeBallot and VAV that is realistic but still provides a guarantee of anonymity. [ABSTRACT FROM AUTHOR]

Programmable logic controllers (PLCs) are heavily used in industrial control systems, because of their high capacity of simultaneous input/output processing capabilities. Characteristically, PLC systems are used in mission critical systems, and PLC software needs to conform real-time constraints in order to work properly. Since PLC programming requires mastering low-level instructions or assembly like languages, an important step in PLC software production is modelling using a formal approach like Petri nets or automata. Afterward, PLC software is produced semiautomatically from the model and refined iteratively. Model checking, on the other hand, is a well-known software verification approach, where typically a set of timed properties are verified by exploring the transition system produced from the software model at hand. Naturally, model checking is applied in a variety of ways to verify the correctness of PLC-based software. In this paper, we provide a broad view about the difficulties that are encountered during the model checking process applied at the verification phase of PLC software production. We classify the approaches from two different perspectives: first, the model checking approach/tool used in the verification process, and second, the software model/source code and its transformation to model checker's specification language. In a nutshell, we have mainly examined SPIN, SMV, and UPPAAL-based model checking activities and model construction using Instruction Lists (and alike), Function Block Diagrams, and Petri nets/automata-based model construction activities. As a result of our studies, we provide a comparison among the studies in the literature regarding various aspects like their application areas, performance considerations, and model checking processes. Our survey can be used to provide guidance for the scholars and practitioners planning to integrate model checking to PLC-based software verification activities. [ABSTRACT FROM AUTHOR]

Information systems, which are responsible for driving many processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the processes underlying an information system. A key challenge in process mining is discovery: Given a log, derive a formal process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the process discovery problem. [ABSTRACT FROM AUTHOR]

With the increasing computing and networking capabilities, IoT devices and edge gateways have become part of a larger IoT–edge–cloud computing continuum, where processing and storage tasks are distributed across the whole network hierarchy, not concentrated only in the cloud. At the same time, this also introduced continuous delivery practices to the development of software components for network-connected gateways and sensing/actuating nodes. These devices are placed on end users' premises and are characterized by continuously changing cyber-physical contexts, forcing software developers to maintain multiple application versions and frequently redeploy them on a distributed fleet of devices with respect to their current contexts. Doing this correctly and efficiently goes beyond manual capabilities and requires an intelligent and reliable automated solution. This paper describes a model-based approach to automatically assigning multiple software deployment plans to hundreds of edge gateways and connected IoT devices implemented in collaboration with a smart healthcare application provider. From a platform-specific model of an existing edge computing platform, we extract a platform-independent model that describes a list of target devices and a pool of available deployment plans. Next, we use constraint solving to automatically assign deployment plans to devices at once with respect to their specific contexts. The result is transformed back into the platform-specific model and includes a suitable deployment plan for each device, which is then consumed by our engine to deploy software components not only on edge gateways but also on their downstream IoT devices with constrained resources and connectivity. We validate the approach with a fleet deployment prototype integrated into a DevOps toolchain used by the partner application provider. Initial experiments demonstrate the viability of the approach and its usefulness in supporting DevOps for edge and IoT software development. [ABSTRACT FROM AUTHOR]

In model-driven software engineering, models are used in all phases of the development process. These models must hold a high quality since the implementation of the systems they represent relies on them. Several existing tools reduce the burden of manually dealing with issues that affect models' quality, such as syntax errors, model smells, and inadequate structures. However, these tools are often inflexible for customization and hard to extend. This paper presents a customizable and extensible model repair framework, PARMOREL, that enables users to deal with different issues in different types of models. The framework uses reinforcement learning to automatically find the best sequence of actions for repairing a broken model according to user preferences. As proof of concept, we repair syntactic errors in class diagrams taking into account a model distance metric and quality characteristics. In addition, we restore inter-model consistency between UML class and sequence diagrams while improving the coupling qualities of the sequence diagrams. Furthermore, we evaluate the approach on a large publicly available dataset and a set of real-world inspired models to show that PARMOREL can decide and pick the best solution to solve the issues present in the models to satisfy user preferences. [ABSTRACT FROM AUTHOR]

Goal-oriented models are gaining significant attention from researchers and practitioners in various domains, especially in software requirements engineering. Similar to other software engineering models, goal models are subject to bad practices (i.e., bad smells). Detecting and rectifying these bad smells would improve the quality of these models. In this paper, we formally define the circular dependency bad smell and then develop an approach based on the simulated annealing (SA) search-based algorithm to detect its instances. Furthermore, we propose two mechanisms (namely, pruning and pairing) to improve the effectiveness of the proposed approach. We empirically evaluate three algorithm combinations, i.e., (1) the base SA search algorithm, (2) the base SA search algorithm augmented with pruning mechanism, and (3) the base SA search algorithm augmented with pruning and pairing mechanisms, using several case studies. Results show that simulated annealing augmented with pruning and pairing is the most effective approach, while the simulated annealing augmented with pruning mechanism is more effective than the base SA search algorithm. We also found that the proposed pruning and pairing mechanisms provide a significant improvement in the detection of circular dependency bad smell, in terms of computation time and accuracy. [ABSTRACT FROM AUTHOR]

Agile is often associated with a lack of architectural thinking causing technical debt but has the advantage of user centricity and a strong focus on value. Model-driven software engineering (MDSE) strongly performs for building a quality architecture and code, but lacks focus on user requirements and tends to consider development as a monolithic whole. The combination of Agile and MDSE has been explored, but a convincing integrated method has not been proposed yet. This paper addresses this gap by exploring the specific combination of MERODE—as an example of a proven MDSE method—with Scrum, a reference agile method offering a concrete (sprint-based) life cycle management on the basis of user stories. The method resulting of this integration is called Agile MERODE; it is driven by user stories, themselves associated with behavior-driven development scenarios. It allows for domain-driven design and permits fast development from domain models by means of code generation. An illustrative example further clarifies the practical application of Agile MERODE, while a case study shows the planning game application in the case's context. While the approach, in its entirety, allows reducing technical debt by building the architecture in a logical, consistent and complete manner, introducing MDSE involves a trade-off with pure value-driven development. Agile MERODE contributes to the state of the art by showing how to increase user centricity in MDSE, how to align model-driven engineering with the Scrum cycle, and how to reduce the technical debt of agile developments yet remaining value-focused. [ABSTRACT FROM AUTHOR]

The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: "SysML – AutomationML" and "SysML4Mechatronics – AutomationML." Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems. [ABSTRACT FROM AUTHOR]

Software architecture models are considered first-class artifacts in current software engineering best practices. Thus, usable and well-understood modeling methods are required for software architects. For this aim, several specific software architecture modeling methods as well as generic design methods included in software development methodologies are available. However, we believe, there is the lack of more specific guidance in current software architecture methods. One of the principal causes of such a lack of specific guidance is the general-purpose nature of these methods. Therefore, further efforts are required to define domain-specific software architecture methods. In this paper, we present SOCAM, a software architecture modeling method for Web Service-Oriented Systems. We illustrate the use of SOCAM with a customization of the well-known SOA test application case: the Sun Adventure Builder system. A comparative analysis of SOCAM with other methods reveals a number of benefits of our method over the other approaches. Also, a survey research method evaluation confirms some of these benefits such as the fact that SOCAM is perceived as more useful than certain general-purpose methods. [ABSTRACT FROM AUTHOR]

The authors reflect on papers that not only indicate the current depth, breadth, and maturity of research and application of model-driven engineering (MDE), but also provide an overview of some of the current open issues. It states that MDE elements such as metamodelling and model transformations are important for leveraging the potential advantages that MDE brings to the software industry. An overview of the 2012 European Conference on Modelling Foundations and Application is offered.

Bug localization is a common task in software engineering, especially when maintaining and evolving software products. This paper introduces a bug localization approach that, in contrast to existing source code approaches, takes advantage of domain information found in the model and the metamodel. Throughout this paper, we present an approach for bug localization in models (BLiM2) that applies the source code ideas for bug localization (textual similarity to the bug description and the Defect Localization Principle) and takes advantage of the domain information from the model and the metamodel. We evaluated our approach in BSH, a real-world industrial case study in the induction hob domain measuring the results in terms of recall, precision, the combination of both the F-measure and the Matthews correlation coefficient. Our study shows that our BLiM2 approach, which combines information from the model and the metamodel for the textual similarity and differentiates between the timespan from the model and metamodel, provides the best results in this work. We also performed a statistical analysis to provide evidence of the significance of the results. The values obtained show that there exist significant differences in the performance of the best BLiM2 approach with the approach used by our industrial partner. Finally, the effect size statistics reveals that the best BLiM2 approach obtains better results in the 78% of the times in the worst case. [ABSTRACT FROM AUTHOR]

The Discrete Event System Specification (DEVS) is a modular and hierarchical Modeling and Simulation (M&S) formalism based on systems theory that provides a general methodology for the construction of reusable models. Well-defined M&S structures have a positive impact when building simulation models because they can be applied systematically. However, even when DEVS can be used to model routing situations, the structures that emerge from this kind of problem are significant due to the handling of the flow of events. Often, the modeler ends with a lot of simulation models that refer to variants of the same component. The goal of this paper is to analyze the routing process domain from a conceptual modeling perspective through the use of a new DEVS extension called Routed DEVS (RDEVS). The RDEVS formalism is conceptually defined as a subclass of DEVS that manages a set of identified events inside a model network where each node combines a behavioral description with a routing policy. In particular, we study the modeling effort required to solve the M&S of routing problems scenarios employing a comparison between RDEVS modeling solutions and DEVS modeling strategies. Such a comparison is based on measures that promote the capture of the behavioral complexity of the final models. The results obtained highlight the modeling benefits of the RDEVS formalism as a constructor of routing processes. The proposed solution reduces the modeling effort involved in DEVS by specifying the event routing process directly in the RDEVS models using design patterns. The novel contribution is an advance in the understanding of how DEVS as a system modeling formalism supports best practices of software engineering in general and conceptual modeling in particular. The reusability and flexibility of the final simulation models, along with designs with low coupling and high cohesion, are the main benefits of the proposal that improve the M&S task applying a conceptual modeling perspective. [ABSTRACT FROM AUTHOR]

Model-Based Software Engineering provides various modelling formalisms for capturing the structural, behavioral, configuration, and intentional aspects of software systems. One of the most widely used kinds of models—domain models—are used during requirements analysis or the early stages of design to capture the domain concepts and relationships in the form of class diagrams. Modellers perform domain modelling to transform the problem descriptions that express informal requirements in natural language to domain models, which are more concise and analyzable. However, this manual practice of domain modelling is laborious and time-consuming. Existing approaches, which aim to assist modellers by automating or semi-automating the construction of domain models from problem descriptions, fail to address three non-trivial aspects of automated domain modelling. First, automatically extracted domain models from existing approaches are not accurate enough to be used directly or with minor modifications for software development or teaching purposes. Second, existing approaches do not support modeller-system interactions beyond providing recommendations. Finally, existing approaches do not facilitate the modellers to learn the rationale behind the modelling decisions taken by an extractor system. Therefore, in this paper, we extend our previous work to facilitate bot-modeller interactions. We propose an algorithm to discover alternative configurations during bot-modeller interactions. Our bot uses this algorithm to find alternative configurations and then present these configurations in the form of suggestions to modellers. Our bot then updates the domain model in response to the acceptance of these suggestions by a modeller. Furthermore, we evaluate the bot for its effectiveness and performance for the test problem descriptions. Our bot achieves median F1 scores of 86%, 91%, and 90% in the Found Configurations, Offered Suggestions, and Updated Domain Models categories, respectively. We also show that the median time taken by our bot to find alternative configurations is 55.5ms for the problem descriptions which are similar to the test problem descriptions in terms of model size and complexity. Finally, we conduct a pilot user study to assess the benefits and limitations of our bot and present the lessons learned from our study in preparation for a large-scale user study. [ABSTRACT FROM AUTHOR]

Business process management plays an important role in the management of organizations. More and more organizations describe their operations as business processes. It is common for organizations to have collections of thousands of business processes, but for reasons of confidentiality these collections are often not, or only partially, available to researchers. On the other hand, research on techniques for managing process model collections, such as techniques for process retrieval, requires large collections for evaluation purposes. Therefore, this paper proposes a technique to generate such collections of process models, based on the properties of real-world collections. Where existing techniques focus on the structure of the process models, the technique proposed in this paper also generates task labels that consists of words from real-life task labels and considers semantic information of node and edge types. We evaluate our technique by applying it to generate two synthetic collections of process models of over 60,000 and over 2,000 models, respectively. We show that the generated synthetic collections have similar properties to the original collections. To the best of our knowledge, this is the first technique that can generate synthetic BPMN models, thus enabling experimentation with process collections that have laboratory-set quantitative parameters and qualitative properties that are based on real-world process model collections. [ABSTRACT FROM AUTHOR]

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified. [ABSTRACT FROM AUTHOR]

Model-based testing relies on a model of the system under test. FineFit is a framework for model-based testing of Java programs. In the FineFit approach, the model is expressed by a set of tables based on Parnas tables. A software product line is a family of programs (the products) with well-defined commonalities and variabilities that are developed by (re)using common artifacts. In this paper, we address the issue of using the FineFit approach to support the development of correct software product lines. We specify a software product line as a specification product line where each product is a FineFit specification of the corresponding software product. The main challenge is to concisely specify the software product line while retaining the readability of the specification of a single system. To address this, we used delta-oriented programming, a recently proposed flexible approach for implementing software product lines, and developed: (1) delta tables as a means to apply the delta-oriented programming idea to the specification of software product lines; and (2) DeltaFineFit as a novel model-based testing approach for software product lines. [ABSTRACT FROM AUTHOR]

An introduction to this special issue of "Software and Systems Modeling," focusing on model-driven engineering, is presented.

An introduction is presented in which the author discusses various reports within the issue on topics including design patterns to the Event-B specification language, reactive systems using signal patterns, and software engineering.

Artefacts play a vital role in software and systems development processes. Other terms like documents, deliverables, or work products are widely used in software development communities instead of the term artefact. In the following, we use the term 'artefact' including all these other terms. Despite its relevance, the exact denotation of the term 'artefact' is still not clear due to a variety of different understandings of the term and to a careless negligent usage. This often leads to approaches being grounded in a fuzzy, unclear understanding of the essential concepts involved. In fact, there does not exist a common terminology. Therefore, it is our goal that the term artefact be standardised so that researchers and practitioners have a common understanding for discussions and contributions. In this position paper, we provide a positioning and critical reflection upon the notion of artefacts in software engineering at different levels of perception and how these relate to each other. We further contribute a metamodel that provides a description of an artefact that is independent from any underlying process model. This metamodel defines artefacts at three levels. Abstraction and refinement relations between these levels allow correlating artefacts to each other and defining the notion of related, refined, and equivalent artefacts. Our contribution shall foster the long overdue and too often underestimated terminological discussion on what artefacts are to provide a common ground with clearer concepts and principles for future software engineering contributions, such as the design of artefact-oriented development processes and tools. [ABSTRACT FROM AUTHOR]

Modeling is a key concept in software engineering education, since students need to learn it in order to be able to produce large-scale and reliable software. Quality tools are needed to support modeling in education, but existing tools vary considerably both in their features and in their strengths and weaknesses. The objective of the research presented in this paper was to help professors and students choose tools by determining which strengths and weaknesses matter most to students, which tools exhibit which of these strengths and weaknesses, and how difficult to use are various tools for students. To achieve this objective, we conducted a survey of the use of modeling tools among students in software engineering courses from Brazil, Canada, USA, Spain, Denmark, UK and China. We report the results regarding the 31 UML tools that 117 participants have used, focusing on the nine tools that the students have used most heavily. Common benefits quoted by students in choosing a tool include simplicity of installing and learning, being free, supporting the most important notations and providing code generation. The most cited complaints about tools include lack of feedback, being slow to use, difficulty drawing the diagrams, not interacting well with other tools and being complex to use. This research also compares the results with the findings of another survey conducted among professors who taught modeling. The results should benefit tool developers by suggesting ways they could improve their tools. The results should also help inform the selection of tools by educators and students. [ABSTRACT FROM AUTHOR]

Given their vital roles in model-based software engineering, the performance of model-related operations (MOs, such as model transformations) must be systematically tested. However, how to produce a set of large input models that conform to structure-related constraints presents a major challenge to such test. This paper proposes a template-based approach to efficient model generation. Firstly, a DSL is provided to describe templates that specify how to generate a valid model that conforms to structure-related constraints. Secondly, a folding semantic is defined to convert templates into a wrapper metamodel. Thirdly, a wrapper model is generated using the existing model generators (e.g., a random model generator) according to the wrapper metamodel. Fourthly, an unfolding semantics is specified to translate the wrapper model into the desired test input. This paper also presents five case studies to evaluate the proposed approach, and the results demonstrate that such approach can generate large models based on structure-related constraints and facilitate the performance testing of MOs. [ABSTRACT FROM AUTHOR]