Software modelling is a creative yet challenging task. Modellers often find themselves lost in the process, from understanding the modelling problem to solving it with proper modelling strategies and modelling tools. Students learning modelling often get overwhelmed with the notations and tools. To teach students systematic modelling, we must investigate students’ practical modelling knowledge and the challenges they face while modelling. We aim to explore students’ modelling knowledge and modelling actions. Further, we want to investigate students’ challenges while solving a modelling task on specific modelling tools. We conducted an empirical study by observing 16 pairs of students from two universities and countries solving modelling tasks for one hour. We find distinct patterns of modelling of class and sequence diagrams based on individual modelling styles, the tools’ interface and modelling knowledge. We observed how modelling tools influence students’ modelling styles and how they can be used to foster students’ confidence and creativity. Based on these observations, we developed a set of guidelines aimed at enhancing modelling education and helping students acquire practical modelling skills. The guidance for modelling in education needs to be structured and systematic. Our findings reveal that different modelling styles exist, which should be properly studied. It is essential to nurture the creative aspect of a modeller, particularly while they are still students. Therefore, selecting the right tool is important, and students should understand how a tool can influence their modelling style.

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Neurodiversity, increasingly recognized in society, encompasses a range of neurological differences, including autism, ADHD, and dyslexia. In software engineering, addressing neurodiversity is crucial to designing accessible and inclusive products that accommodate diverse user needs. Despite growing interest, a clear understanding of how to create neurodivergent-inclusive software remains underdeveloped. To help bridge this gap, we developed a conceptual model that identifies key neurodivergent concepts and relationships to guide software requirements elicitation. This model was evaluated through a survey involving 100 participants, including 37 neurodivergent individuals. These initial evaluation results are encouraging and indicate the model has the potential to advance the creation of software accessible to neurodivergent users. Moreover, the survey also revealed several necessary improvements towards successful requirements elicitation support. To this end, we aim to ensure the completeness of the collected information and identify potential contradictions among accommodating strategies, as a foundation for developing concrete support tools for requirements elicitation. Based on the complete conceptual model, these tools will integrate into a dedicated elicitation method, built on top of traditional elicitation methods, such as focus groups or workshops, and will be further enhanced by a large language model-based assistant. These results will support requirements engineers to effectively elicit neurodivergent-inclusive requirements.

Introduction : Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and inclusion in software development to create safer, more equitable technology. Method : This research is based on insights from an academic meeting in June 2023 involving 23 software engineering researchers and practitioners. We used the collaborative discussion method 1-2-4-ALL as a systematic research approach and identified six themes around the theme “challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI).” We identified benefits, harms, and future research directions for the four main themes. Then, we discuss the remaining two themes, AI & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes. Results : This research explores the key challenges and research opportunities for promoting SDDI, providing a roadmap to guide both researchers and practitioners. We underline that research around SDDI requires a constant focus on maximizing benefits while minimizing harms, especially to vulnerable groups. As a research community, we must strike this balance in a responsible way.

Background: Despite having several advantages, software modelling remains unpopular for developers. Similarly, university students do not see the benefits of software modelling in the university curriculum. Prior research show the lack of guidance for students to do so.

Research on model-based engineering (MBE) has occasionally touched upon the relationship between development processes and concrete MBE practices. However, the alignment of these elements has rarely been the central focus of these studies. As a result, important questions regarding the alignment of MBE and development processes, as well as the impact of development processes on the utilization and success of MBE, have remained unanswered. To address this research gap, we conducted a multiple-case study involving 14 individuals from nine different companies, conducting a total of 12 interviews. Building upon seven propositions derived from existing literature, our investigation sought to understand how MBE is aligned with the development process and explore the application of MBE in this context. Additionally, we identified challenges and needs in this area. Our findings challenge some previously reported results, such as the perceived conflicts between agile development processes and MBE. Furthermore, we unearthed previously unreported issues, like the importance of considering the perspectives of tool vendors in MBE discussions. Overall, this paper makes a significant contribution by providing a comprehensive and up-to-date perspective on how MBE is integrated into development processes, along with an examination of the social and organizational aspects inherent to these processes. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum’s smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records.

Abstract Designing software is an activity in which software developers think and make design decisions that shape the structure and behavior of software products. Designing software is one of the least understood software engineering activities. In a collaborative design setting, various types of distances can lead to challenges and effects that potentially affect how software is designed. To contribute to a better understanding of collaborative software design, we investigate how communication gaps caused by social and geographic distances affect its design thinking and the creativity of its discussions. To this end, we conducted a multiple‐case study exploring the design thinking and creativity of colocated and distributed software developers in a collaborative design setting. Compared with colocated developers, distributed developers spend less time on exploring the problem space, which could be related to different sociotechnical challenges, such as lack of awareness and common understanding. Distributed development does not seem to affect the creativity of their activities. Developers engaging in collaborative design need to be aware that problem space exploration is reduced in a distributed setting. Unless distributed teams take compensatory measures, this could adversely affect the development. Regarding the effect distance has on creativity, our results are inconclusive and further studies are needed.

Agile methods have become mainstream even in large-scale systems engineering companies that need to accommodate different development cycles of hardware and software. For such companies, requirements engineering is an essential activity that involves upfront and detailed analysis which can be at odds with agile development methods. This paper presents a multiple case study with seven large-scale systems companies, reporting their challenges, together with best practices from industry. We also analyze literature about two popular large-scale agile frameworks, SAFe® and LeSS, to derive potential solutions for the challenges. Our results are based on 20 qualitative interviews, five focus groups, and eight cross-company workshops which we used to both collect and validate our results. We found 24 challenges which we grouped in six themes, then mapped to solutions from SAFe®, LeSS, and our companies, when available. In this way, we contribute a comprehensive overview of RE challenges in relation to large-scale agile system development, evaluate the degree to which they have been addressed, and outline research gaps. We expect these results to be useful for practitioners who are responsible for designing processes, methods, or tools for large scale agile development as well as guidance for researchers.

Context: Designing software is an activity in which software developers think and make design decisions that ultimately shape the structure and behavior of software products. Currently, designing software is one of the least understood activities in which software developers engage. In a collaborative design setting, distances such as geographic, cultural, or social distance can lead to socio-technical challenges that potentially affect the way software is designed.

Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of models in RE, with respect to the automotive domain. In this paper, we report our findings from a multiple-case study with two automotive companies, collecting interview data from 14 practitioners. Our results show that models are used for a variety of different purposes during RE in the automotive domain, e.g. to improve communication and to handle complexity. However, these models are often used in an unsystematic fashion and restricted to few experts. A more widespread use of models is prevented by various challenges, most of which align with existing work on model use in a general sense. Furthermore, our results indicate that there are many potential benefits associated with future use of models during RE. Interestingly, existing research does not align well with several of the proposed use cases, e.g. restricting the use of models to informal notations for communication purposes. Based on our findings, we recommend a stronger focus on informal modelling and on using models for multi-disciplinary environments. Additionally, we see the need for future work in the area of model use, i.e. information extraction from models by non-expert modellers.

T-Reqs is a text-based requirements management solution based on the git version control system. It combines useful conventions, templates and helper scripts with powerful existing solutions from the git ecosystem and provides a working solution to address some known requirements engineering challenges in large-scale agile system development. Specifically, it allows agile cross-functional teams to be aware of requirements at system level and enables them to efficiently propose updates to those requirements. Based on our experience with T-Reqs, we i) relate known requirements challenges of large-scale agile system development to tool support; ii) list key requirements for tooling in such a context; and iii) propose concrete solutions for challenges.

Problem: The involvement of external stakeholders in capstone projects and project courses is desirable due to its potential positive effects on the students. Capstone projects particularly profit from the inclusion of an industrial partner to make the project relevant and help students acquire professional skills. In addition, an increasing push towards education that is aligned with industry and incorporates industrial partners can be observed. However, the involvement of external stakeholders in teaching moments can create friction and could, in the worst case, lead to frustration of all involved parties. Contribution: We developed a model that allows analysing the involvement of external stakeholders in university courses both in a retrospective fashion, to gain insights from past course instances, and in a constructive fashion, to plan the involvement of external stakeholders. Key Concepts: The conceptual model and the accompanying guideline guide the teachers in their analysis of stakeholder involvement. The model is comprised of several activities (define, execute, and evaluate the collaboration). The guideline provides questions that the teachers should answer for each of these activities. In the constructive use, the model allows teachers to define an action plan based on an analysis of potential stakeholders and the pedagogical objectives. In the retrospective use, the model allows teachers to identify issues that appeared during the project and their underlying causes. Drawing from ideas of the reflective practitioner, the model contains an emphasis on reflection and interpretation of the observations made by the teacher and other groups involved in the courses. Key Lessons: Applying the model retrospectively to a total of eight courses shows that it is possible to reveal hitherto implicit risks and assumptions and to gain a better insight into the interaction between external stakeholders and students. Our empirical data reveals seven recurring risk themes that categorise the different risks appearing in the analysed courses. These themes can also be used to categorise mitigation strategies to address these risks proactively. Additionally, aspects not related to external stakeholders, e.g., about the interaction of the project with other courses in the study programme, have been revealed. The constructive use of the model for one course has proved helpful in identifying action alternatives and finally deciding to not include external stakeholders in the project due to the perceived cost-benefit-ratio. Implications to Practice: Our evaluation shows that the model is a viable and useful tool that allows teachers to reason about and plan the involvement of external stakeholders in a variety of course settings, and in particular in capstone projects.

Project success in the automotive industry is highly influenced by requirements engineering (RE), for which communication and organisation structure play a major role, much due to the scale and distribution of these projects. However, empirical research is scarce on these aspects of automotive RE and warrants closer examination. Therefore, the purpose of this paper is to identify problems or challenges in automotive RE with respect to communication and organisation structure. Using a multiple-case study approach, we collected data via 14 semi-structured interviews at one car manufacturer and one supplier. We tested our findings from the case study with a questionnaire distributed to practitioners in the automotive industry. Our results indicate that it is difficult but increasingly important to establish communication channels outside the fixed organisation structure and that responsibilities are often unclear. Product knowledge during early requirements elicitation and context knowledge later on is lacking. Furthermore, abstraction gaps between requirements on different abstraction levels leads to inconsistencies. For academia, we formulate a concrete agenda for future research. Practitioners can use the findings to broaden their understanding of how the problems manifest and to improve their organisations.

While several benefits of using models in software engineering have been observed in practice, the adoption of modeling remains low. Multiple challenges of using models, especially related to tools, have been reported both for industrial use and for education. However, there is a lack of systematic, empirical investigations of the challenges in modeling education and their relation to industrial challenges. Therefore, we conducted a multiple-case study with two cases, in the U.S and Sweden, focusing on students' perceptions towards tooling and UML in education. Our data collected from 369 student evaluation surveys, enriched with qualitative data, shows that the students' perception of modeling tools depends not only on the complexity of tools, but rather on multiple contextual factors, including tool characteristics, scope of course and project contents, nature of the required models, and the tools' role in generating executable artifacts. We conclude that there is a need for tailoring modeling tools for education beyond focusing on simplification and usability. Furthermore, due to the broad diversity within the modeling domain, there is a need for adapting the use of tools to the specific curriculum and course learning objectives.

Formalising requirements has the potential to solve problems arising from deficiencies in natural language descriptions. While behavioural requirements are rarely described formally in industry, increasing complexity and new safety standards have renewed the interest in formal specifications. The goal of this paper is to explore how behavioural requirements for embedded systems can be formalised and aligned with verification tasks. Over the course of a 2.5-year project with industry, we modelled existing requirements from a safety-critical automotive software function in several iterations. Taking practical limitations and stakeholder preferences into account, we explored the use of models on different abstraction levels. The final model was used to generate test cases and was evaluated in three interviews with relevant industry practitioners. We conclude that models on a high level of abstraction are most suitable for industrial requirements engineering, especially when they need to be interpreted by other stakeholders.

Motivated by their success in software development, companies implement agile methods and their practices increasingly for software-intense, large products, such as cars, telecommunication infrastructure, and embedded systems. Such systems are usually subject to safety and regulative concerns as well as different development cycles of hardware and software. Consequently, requirements engineering involves upfront and detailed analysis, which can be at odds with agile (software) development. In this paper, we present results from a multiple case study with two car manufacturers, a telecommunications company, and a technology company that are on the journey to introduce organization wide continuous integration and continuous delivery to customers. Based on 20 qualitative interviews, 5 focus groups, and 2 cross-company workshops, we discuss possible scopes of agile methods within system development, the consequences this has on the role of requirements, and the challenges that arise from the interplay of requirements engineering and agile methods in large-scale system development. These relate in particular to communicating and managing knowledge about a) customer value and b) the system under development. We conclude that better alignment of a holistic requirements model with agile development practices promises rich gains in development speed, flexibility, and overall quality of software and systems.

It has been stated that industrial-grade modelling tools are unsuitable for teaching modelling. We assume, however, that the experiences of the teachers and the students is strongly connected to the support available. In this paper, we present our experience with a university course on software modelling. In the first year of the course, we used a commercial modelling tool, in the second year the open-source alternative Papyrus. Our quantitative analysis shows that the industrial-grade modelling tools with all their complexity did not have a negative impact on the students' experience of modelling. We analyse why our experience differs from published accounts and conclude that the availability of a tool champion and tailored instruction material is key. From this, we derive lessons learned and give recommendations on how to successfully use industrial-strength modelling tools in the classroom.

Along with technological developments and increasing competition there is a major incentive for companies to produce and market high quality products before their competitors. In order to conquer a bigger portion of the market share, companies have to ensure the quality of the product in a shorter time frame. To accomplish this task companies try to automate their test processes as much as possible. It is critical to investigate and understand the problems that occur during different stages of test automation processes. In this paper we report on a case study on automatic analysis of non-functional test results. We discuss challenges in the face of continuous integration and deployment and provide improvement suggestions based on interviews at a large company in Sweden. The key contributions of this work are filling the knowledge gap in research about performance regression test analysis automation and providing warning signs and a road map for the industry.

Modern automotive embedded systems are developed by Original Equipment Manufacturers (OEM) together with multiple suppliers. A key problem for a supplier is to allocate an OEM's requirements specification to their own subsystem design. This is a difficult manual task especially on complex systems and it requires expert knowledge about the system design. To address this problem, this paper presents a design science research to develop and evaluate a Requirements Allocation Assistant tool (RAA). The tool provides functionality to search through and filter requirements and system models to enable efficient requirements allocation even in the presence of complexity.RAA is built on top of the EATOP/Eclipse framework using EAST-ADL as system modelling language. The tool was evaluated and validated during a qualitative usability study with 17 engineers active in the Swedish automotive industry. Key findings are that searching is used to learn about a system, whereas filtering is used to narrow down a set of candidate elements of the system design. Engineers request further support in narrowing down a set of candidate elements and in checking that an allocation is correct.

3D data collected by a laser scanner has great potential for robotic applications. Exact geometrical models of the environment surrounding the robot can be created from these point clouds. But, before creating any model, the 3D point cloud has to be segmented and depending on the size and quality of the point cloud, this can be a very challenging task. This article describes a robust 3D scan segmentation technique, which is capable of segmenting a 3D point cloud in a short amount of time. The results of the segmentation are used to assist a teleoperator to manoeuvre a robot through an unknown environment. Our segmentation approach copes with indoor and outdoor environments, using only a minimum of assumptions, which makes it very robust. A 3D visualisation illustrates the segmentation results in a clear and user-friendly way.