Sustainability is gaining increasing importance in software engineering education, as future software engineers must understand the environmental and societal effects of the systems they create. To prepare students for these challenges, it is essential to integrate sustainability into practical courses where students can explore its principles and real-world implications. However, there are still limited examples of how sustainability can be effectively embedded into existing university curricula, highlighting a significant gap in educational practice. In this paper, we describe an experience report of a practical and agile-oriented undergraduate software engineering course that integrates sustainability. We found that the students were successful in identifying various effects of a software system on different sustainability dimensions. Furthermore, sustainability aspects can be seamlessly integrated into existing courses with minimal effort. Therefore, we share our learning materials with the hope that they will inspire and be reused by other software engineering educators.

Abstract In the wake of a rapidly growing global focus on sustainable practices, the year 2023 marked a significant regulatory milestone, with the enactment of the Corporate Sustainability Reporting Directive. This directive significantly expands the scope of sustainability reporting, encompassing a broader array of enterprises, including large-scale, and small- and medium-sized enterprises within the EU. This regulatory development has profound implications for the software industry, as these companies need to provide comprehensive sustainability reporting for their software products. However, the industry still lacks models, tools, and methodologies for quantitatively assessing sustainability indicators during an agile software development process. In this paper, we introduce SustainScrum, a customized Scrum process model that incorporates sustainability evaluations into the development lifecycle of software products. More precisely, SustainScrum integrates the assessment of sustainability aspects into the backlog, user story management, and development stages of the Scrum process. Its primary objective is to ensure that sustainability considerations are systematically captured, evaluated, and addressed throughout the development process. It integrates the computation of quantitative sustainability indicators thereby advancing the ability to address sustainability challenges within software engineering practices. We performed an initial validation, investigating the applicability of SustainScrum on an open-source, publicly available requirements data set for agile development.

The increased emphasis on competency management and learning objectives in higher education has led to a rise in Learning Analytics (LA) applications. These tools play a vital role in measuring and optimizing learning outcomes by analyzing and interpreting student-related data. \nLA tools furthermore provide course instructors with insights on how to refine teaching methods and material and address diversity in student performance to tailor instruction to individual needs. \nThis tool demonstration paper introduces our Learning Analytics Dashboard, designed for an introductory Python programming course. With a focus on gender diversity, the dashboard analyzes graded Jupyter Notebooks, to provide insights into student performance across assignments and exams. An initial assessment of the dashboard, applying it to our Python programming course in the previous year, has provided us with interesting insights and information on how to further improve our class and teaching materials. We present the dashboard's design, features, and outcomes while outlining our plans for its future development and enhancement.

Assurance of Learning and Competency-Based Education are increasingly important in higher education, not only for accreditation or transfer of credit points. Learning Analytics is crucial for making educational goals measurable and actionable, which is beneficial for program managers, course instructors, and students. \nWhile universities typically have an established tool landscape where relevant data is managed, information is typically scattered across various systems with different responsibilities and often only limited capabilities for sharing data. This diversity, however, significantly hampers the ability to analyze data, both on the course and curriculum level.\nTo address these shortcomings and to provide program managers, course instructions, and students with valuable insights, we devised an initial concept for a Multi-Level Shared Learning Analytics Framework to provide consistent definition and measurement of learning objectives, as well as tailored information, visualization, and analysis for different stakeholders.\nIn this paper, we present the results of initial interviews with stakeholders, devising core features. In addition, we assess potential risks and concerns that may arise from the implementation of such a framework and data analytics system. As a result, we identified six essential features and six main risks to guide further requirements elicitation and development of our proposed framework.

In recent years, computer science education has increasingly focused on the development and application of automated code assessment methods. Test-based assessments of student submissions generate large quantities of data that could be used for learning analytics. Existing literature highlights a predominant use of unit tests for grading rather than for extracting insights into students' challenges in learning programming concepts. Moreover, competence models, which systematically outline required learning outcomes, are increasingly important during curriculum design. We introduce a novel approach for systematically developing programming assignments aligned with competence models. This method also aids educators in creating unit tests that support competence-based learning analytics. As our method maps competences to individual test cases and thereby quantifies a student's proficiency in an assignment, this helps educators to evaluate, whether students have the ability to understand and master the required competences. Experimental application of our method demonstrated enhanced clarity in understanding student assignments and a considerable improvement in the quality of learning analytics data.

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM Computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas.

The assessment of source code in university education is a central and important task for lecturers of programming courses. In doing so, educators are confronted with growing numbers of students having increasingly diverse prerequisites, a shortage of tutors, and highly dynamic learning objectives. To support lecturers in meeting these challenges, the use of automated programming assessment systems (APASs), facilitating formative assessments by providing timely, objective feedback, is a promising solution. Measuring the effectiveness and success of these platforms is crucial to understanding how such platforms should be designed, implemented, and used. However, research and practice lack a common understanding of aspects influencing the success of APASs. To address these issues, we have devised a success model for APASs based on established models from information systems as well as blended learning research and conducted an online survey with 414 students using the same APAS. In addition, we examined the role of mediators intervening between technology-, system- or self-related factors, respectively, and the users' satisfaction with APASs. Ultimately, our research has yielded a model of success comprising seven constructs influencing user satisfaction with an APAS.

Sustainability has become a widely discussed topic in software and requirements engineering. However, in the context of agile software development processes, little is known about how to identify potential sustainability effects of software systems. The goal of our research is to investigate how requirements engineering knowledge on sustainability can be incorporated into agile frameworks like Scrum. In this experience paper, we present the results of two case studies from ongoing agile development projects where the Sustainability Awareness Framework was applied. Within workshops, more than 20 potential sustainability effects have been identified for each of the systems based on their product visions. Furthermore, we analyzed the product backlogs and found that more than half of the backlog items of both systems can be related to at least one of the identified sustainability effects. This suggests that a sustainability analysis of backlog items can support the early identification of potential sustainability effects in Scrum. Our findings suggest that the Sustainability Awareness Framework can be successfully used by practitioners in agile settings based on the guidelines provided by the Sustainability Awareness Framework.

Sustainability is becoming increasingly important in software engineering, especially in requirements engineering. Several requirements engineering approaches have already been proposed to deal with the question of how impacts of requirements on sustainability dimensions can be identified and managed. However, in contrast to requirements engineering, little is known about how to identify the potential sustainability effects of software systems in agile software development and how to foster the agile development of sustainable software. The goal of our research is to investigate how sustainability can be incorporated into Scrum. Our ongoing work targets the development of a Sustainability-Aware Scrum Framework that integrates requirements engineering knowledge into Scrum to raise awareness around the sustainability impacts of software systems and to make software systems more sustainable. The main contribution of our work is a set of recommendations on how the Scrum framework, as the major agile development process, can be extended towards sustainability. Furthermore, we discuss an evaluation plan, present a prototype of a software tool that supports tracking possible sustainability impacts of user stories, and provide preliminary results.

Abstract Various approaches toward the development of sustainable software systems have been proposed by the requirements engineering community over the last decade. We conducted a systematic mapping study, analyzed 55 publications, and identified 29 approaches that have been published since the year 2000. We analyzed how the approaches evolved over time and how the publications and authors are influenced by each other. Furthermore, the approaches are analyzed in terms of their supported requirements engineering activities, along with the evidence provided in the publications. Additionally, we also analyzed which sustainability definitions have been used, if an iterative application of the approaches is discussed, and if the approaches also provide a tool-support for practitioners. We noticed an increase of publications on requirements engineering approaches toward sustainability in the last years, whereas a majority discuss sustainability based on the same multi-dimensional concept. Although different case studies have been already conducted, we noticed a lack of an industrial application. Our main findings concern the need of an evaluation on how the proposed requirements engineering approaches can also be applied in agile software development processes. Additionally, we also promote the development of supporting software tools to support practitioners in adapting the proposed approaches.

In programming education for non-computer science students, high diversity exists among our students, including gender, culture, age, educational background, and work experience. Introductory programming courses traditionally face high drop-out rates and poor performance, and students often perceive learning to program as difficult. Current research on diversity in programming education has primarily focused on gender differences, thus neglecting the influence of other diversity dimensions on students' performance. The project Diversity in Programming Education (DIPE) aims to fill this gap by identifying how heterogeneous groups of students can be best supported. Based on our findings, we develop a didactic concept with accompanying teaching and learning material to support different diversity dimensions actively in programming education. The concept includes competence models for measuring competences and individual learning paths. To support a flat learning curve further in university programming courses in the future, we closely work with schools and educational centres for teachers to integrate algorithmic thinking into school curricula. We empirically evaluate our concept using a mixed methods approach. In particular, we will explore the effects of our concept in our programming course and will investigate the value of these effects as perceived by lecturers and students.

No matter whether young or old, almost everyone likes to play games. Even though digital games have the opportunity to motivate students and foster digital skills, games in an educational context are typically described as a minor matter in class. However, games can provide an environment to work intensively on and consolidate topics from the classroom. Especially if students have the task of developing a game themselves, it is necessary to work on the topic in depth while the content is learned and anchored playfully. As part of the COOL Informatics project, which had the goal of spreading digital education and programming in the educational sector, a Game Design Challenge was launched. In this challenge, pupils, and teachers from all over Austria were invited to develop their own educational games. It is planned to relaunch the Game Design Challenge in 2021 with the goal that more and more students can be introduced to the basics of Computational Thinking.

Despite great effort in research and teaching as well as in raising women's quotas in the public sector and in enterprises, a gender gap in computer science can still be observed. The reasons are manifold, including the lack of interest of girls because of existing stereotypes of male nerds, misconceptions in the field of computer science, and concrete differences in students' self-concept and performance. Girls do not choose schools or studies in the field of computer science because working on computers or coding is unattractive to them. Research shows poorer performance, missing interest, and lower self-concept for girls in secondary computing education as well as a high dropout rate of female students in computer science courses at the bachelor's level. Software development exams contribute to those high dropout rates, and women often choose a different subject or even stop their university education altogether. This leads to fewer female employees in the field of computer science, and this situation calls for special measures at several levels. The current paper provides an overview of the initiatives of our university that aim to address several aspects of the gender gap. The programs offered aim to (1) increase interest in computer science and recruit (highly) gifted girls for our talent programs, (2) recruit and support female bachelor's and master's students in computer science, and (3) redesign programming courses and teaching materials to reduce the gender gap in performance and support, especially for female students. This study also describes a "cyber tutoring" program in which highly gifted girls between 13 and 16 years collaborate with and are supervised by female role models in higher positions in STEM. The evaluation results gained in the first year of this case study are promising and suggest that the extension and further development of the program would be advantageous.

COOL Informatics (COOL stands for Cooperative Open Learning) is a teaching concept for beginning programmers that is based on brain-supporting teaching methods and materials. It includes several forms of cooperative learning like peer tutoring, pair programming, and talents exchange. We introduced the concept in a Java programming course of our Business Informatics bachelor program in 2018 and have been able to improve the learning outcomes (exam results) and to reduce drop-out rates compared to previous years and to reference groups of 2018.

Continuous requirements elicitation is an essential aspect of software product evolution to keep systems aligned with changing user needs. However, current requirements engineering approaches do not explicitly address sustainability in the evolution of systems. Reasons include a lack of awareness and a lack of shared understanding of the concept of sustainability in the RE community. Identifying and analysing the effects of requirements regarding sustainability is challenging, as these effects can have an impact on multiple stakeholders and manifest themselves in one or more sustainability dimensions at different points in time. We argue that crowd-focused semi-automated requirements engineering allows the engagement of a large number of stakeholders (including users and domain experts) in a continuous cycle of negotiation regarding the potential effects of requirements on sustainability. Based on a motivating scenario, we introduce the idea of a platform for crowd-focused requirements engineering that supports the evolution towards sustainability. For the three key aspects of this platform, we present our ongoing work and discuss early results. We outline how the platform can be utilised to improve the broader awareness and understanding of sustainability, not only for the involved crowd but also for researchers and society in general.

Enterprise Resource Planning (ERP) system vendors need to customize their products according to the domain-specific requirements of their customers. Systematic reuse of features and related ERP product customizations would improve software quality and save implementation time. In our previous research, we have developed a tool-based approach supporting feature-based reuse of ERP product customizations. Our tool environment automatically infers reusable features from requirements and their associated implementation artifacts. Furthermore, it allows domain experts to search for features based on natural language requirement descriptions representing the needs of new customers. Matching features can be automatically deployed to a new ERP product. In this paper, we present an industrial evaluation of our tool-based approach conducted together with an Austrian small- to medium-sized enterprise. A domain expert used our approach to identify matching features for 20 randomly selected requirements for new customer products and identified matching features for 17 of the 20 requirements. We compared the time needed to identify and deploy the candidate features with the time required to implement the requirements from scratch. We found that, in total, over 60% implementation time can be saved by applying our reuse approach presented in this case study.

The study presented in this paper aims to provide evidence for the hypothesis that software product line research has been changing and that the works in industry and academia have diverged over time. We analysed a subset (140) of all (593) papers published at the Software Product Line Conference (SPLC) until 2017. The subset was randomly selected to cover all years as well as types of papers. We assessed the research type of the papers (academic or industry), the kind of evaluation (application example, empirical, etc.), and the application domain. Also, we assessed which product line life-cycle phases, development practices, and topics the papers address. We present an analysis of the topics covered by academic vs. industry research and discuss the evolution of these topics and their relation over the years. We also discuss implications for researchers and practitioners. We conclude that even though several topics have received more attention than others, academic and industry research on software product lines are actually rather in line with each other.

In recent years, there has been growing interest in research on sustainability in software engineering. Despite active research in this area, there is still a lack of understanding of how sustainability is perceived by software professionals. To understand how software sustainability is currently dealt with in practice, we performed an interview study with 10 software project team leads from nine companies in Austria. Our study shows that practitioners regard software sustainability as important but are technically minded with respect to sustainability. Organizational and economic issues are addressed, but environmental considerations are missing. The perceived influence of various project factors on sustainability is partly diverse, suggesting that the meaning of sustainability needs to be refined for the specific project and application context.

Abstract Sustainable living, ie, living within the bounds of the available environmental, social, and economic resources, is the focus of many present‐day social and scientific discussions. But what does sustainability mean within the context of software engineering? In this paper, we undertake a comprehensive analysis of 8 case studies to address this question within the context of a specific software engineering approach, software product line engineering (SPLE). We identify the sustainability‐related characteristics that arise in present‐day studies that apply SPLE. We conclude that technical and economic sustainability are in prime focus on the present SPLE practice, with social sustainability issues, where they relate to organisations, also addressed to a good degree. On the other hand, the issues related to the personal sustainability are less prominent, and environmental considerations are nearly completely amiss. We present feature models and cross‐relations that result from our analysis as a starting point for sustainability engineering through SPLE, suggesting that any new development should consider how these models would be instantiated and expanded for the intended sociotechnical system. The good representation of sustainability features in these models is also validated with 2 additional case studies.

Manufacturing execution systems (MES) are key elements of industrial automation systems. MES can be deployed at different levels of scale from a single site or plant to a company with globally distributed production sites all over the world. Establishing or extending an MES is a complex process, which requires taking the already existing software and system architecture into account in addition to the desired MES features. We developed an approach and an associated tool to support the process of creating offers for customer-specific MES solutions based on a vendor-specific automation platform. We define architecture variants for selecting a specific MES feature set and for supporting different MES expansion stages. Additionally, we provide an architecture modeling approach to explore the integration with existing software and system infrastructures. The approach has been applied at the STIWA Group, a vendor of MES for industrial production lines.

Design decisions are made throughout the design process of a new software system or the evolution of an existing system. The context in which a system is developed influences these decisions themselves and the way they are made. There are only a few empirical studies regarding architectural decision-making or concerning how the decision-making process is executed. In this paper, we report an analysis of expert interviews regarding architectural decision-making to gain insight into how decision-making is organized in different organizational contexts. We base our analysis on interviews conducted in a previous study, where we talked to 25 software architects, team leads, and senior developers from 22 different companies in ten different countries about architectural decision-making and documentation. In this paper, we specifically analyze the interview transcripts with regard to the decision-making process. We identified eight different categories of main factors influencing how, when, and by whom decisions are made. We also present decision-making scenarios and relate them to the discovered influence factors. Results show that, apart from organizational factors, individual factors and cultural factors seem to have about the same influence as business and project factors. Company size and domain do not influence the decision-making process as much as one might expect.

Sustainability encompasses a wide set of aims: ranging from energy efficient software products (environmental sustainability), reduction of software development and maintenance costs (economic sustainability), to employee wellbeing (social sustainability). This panel brings together researchers and practitioners to explore the role that sustainability will play in software product line engineering. The panel aims to explore how sustainability manifests itself in domain engineering, via study of, for instance, sustainability patterns in domain analysis, architectural decisions motivated by specific sustainability concerns, types of variability that results from sustainability considerations, as well as engineering of sustainability as a domain itself. This panel explores challenges in research and practice for Sustainability in Software Product Line Engineering.

Systematic variability management is an important prerequisite for successful software reuse. However, it requires significant effort and extensive domain knowledge to document and maintain information on variability. In this paper we present a tool-supported approach which supports semi-automatically inferring variability information from customized standard software products. The approach does not only enable the identification and documentation of variability information based on existing products, it is also capable of incrementally updating this information. To guarantee quick access to reusable code artifacts (e.g. requirements, features or software components), the presented solution stores these artifacts together with related requirements and a generated variability model in an asset repository. The tool-supported approach has been applied to customizations of Microsoft Dynamics AX ERP systems. Our experiences highlight the potential and benefits of our approach compared to manually gathering information on software variability.

We have recently developed a tool-supported approach for the transitioning from implicit to explicit variability management of existing software products. Such an approach requires the definition of a variability model that is then linked to the existing product architecture to document how the variability is realized in the architecture. As the existing architecture has typically not been developed with variability in mind, different inconsistencies can occur when linking the variability model to the architecture. In this paper we classify the different inconsistencies, show how they can be detected, and propose different refactoring strategies to support the systematic alignment of variability model and architecture. As a proof-of-concept, the identified strategies have been applied in a first case study of introducing explicit variability management in our own architecture toolkit.

This demonstration presents an approach that facilitates variability implementation, management, and tracing from architectural modeling to implementation. A tool suite is provided that integrates aspect-oriented and model-driven software development into product line engineering.

Software product line engineering aims to reduce development time, effort, cost, and complexity by taking advantage of the commonality within a portfolio of similar products. The effectiveness of a software product line approach directly depends on how well feature variability within the portfolio is implemented and managed throughout the development lifecycle, from early analysis through maintenance and evolution. This paper presents an approach that facilitates variability implementation, management and tracing by integrating model-driven and aspect-oriented software development. Features are separated in models and composed by aspect-oriented composition techniques on model level. Model transformations support the transition from problem to solution domain. Aspect-oriented techniques enable the explicit expression and modularization of variability on model, code, and template level The presented concepts are illustrated with a case study of a home automation system.

This paper explains the challenges we experienced when introducing a software product family approach in Siemens business groups. Our vision is a complete and easily accessible cookbook with advice on how to start such an approach. In a first attempt, we identified a collection of more or less successful best practices. On the suggestions and the open questions we are going to present in this paper, we search validation by practitioners in the field