Context: Research software is instrumental in producing research results. It plays a special role in advancing scientific discovery through tasks such as data analysis, simulation, and visualisation. However, despite its importance, the organisations that produce research software face challenges in managing research software projects. Existing frameworks for software engineering often overlook the needs of research software, including research software engineering practices and open science principles. Without clear guidance, organisations that produce research software must develop and invent new techniques for research software engineering, which is a slow and costly process. Objective: This work presents RSMM, a maturity model designed to improve organisational practices in research software project management. Methods: The initial version of RSMM was developed through a systematic literature review. Expert interviews were then conducted to evaluate and refine the model. Finally, multiple case studies were carried out to validate RSMM and demonstrate its applicability in real-world settings. Results: The final version of RSMM (v1.0) comprises 79 best practices, 17 capabilities, and 10 maturity levels, organised into 4 focus areas: ‘Software Project Management’ , ‘Research Software Management’ , ‘Community Engagement’ , and ‘Software Adoptability’ . We provide a comprehensive analysis of RSMM v1.0 and demonstrate its practical applicability through two illustrative case studies. Conclusion: The RSMM is designed to help organisations in evaluating and improving their research software project management by assessing a project’s current maturity level and providing best practices across four focus areas to guide its progression.

Abstract Error messages are one of the primary ways software developers communicate with database management systems in SQL query writing tasks. Even though reading and interpreting error messages is a significant part of a software developer’s work, the error messages of SQL compilers have received criticism in terms of readability and their perceived detrimental effects on user experience. Consequently, redesigned SQL error messages have also been proposed to tackle the problems in current error messages. In this study, we examine the effects of enhanced error messages on query writing from several perspectives. The results indicate that when compared to PostgreSQL error messages, the enhanced error messages facilitate faster error fixing, as well as perceived benefits in error finding and error recovery confidence. Our results are applicable in industry, where development time is of significant importance, as well as in educational contexts, where user experience, user confidence, and perceived support in learning play an important role.

The Internet of Things (IoT) content and curricula is a recently emerged trend for computer science and software engineering educators. IoT as a paradigm is often described as an all-encompassing new phenomenon covering homes, industries, governments, and the environment. For that reason, IoT topics have become a part of computer science and software engineering programs. Existing research has been conducted on the development of IoT curricula but as this work is fairly recent the evaluation of those approaches requires further work. This paper presents a case study from a capstone course within a three-course IoT specialization. We evaluate student capstone projects using the thematic analysis method in order to assess our IoT specialization studies. As a result, we present an overview of how the students see the IoT, and what kind of IoT projects they design and implement. Most often the student projects implemented home automation projects. Often these prototypes lacked connectivity and communication capabilities to other systems. An IoT characteristic that is most rarely seen in student projects is 'physical actions in the environment.'

Sustained participation is critical to the success of digital citizen-science initiatives, yet much of the current literature focuses on mapping people’s motives to engage without considering the extent to which participation is sustained over time. We conducted a year-long experimental study (n=85) “in-thewild” to explore the effects of human-value orientations on the use of digital citizen-science tools. Participants took part in both the co-design and use of digital citizen-science tools in Lappeenranta, Finland from 2018–2019. Our statistical analysis finds evidence of relations between value orientations, sustained participation, and the number and quality of digital interactions. Specifically, we find that value orientations are linked with different usage patterns. For instance, people with a stronger openness-to-change (OTC) values tended to use the mobile application to check others’ submissions, even when they had nothing to submit, whereas people with stronger security values mostly used the application when they had something relevant to submit. Further understanding the influence of human values in digital citizen science is a promising area for future research that could contribute to a) guide the design of incentive mechanisms, b) understand user experiences in online communities, and c) inform the design and evaluation of digital citizen-science technologies.

Personalization is an upcoming trend in gamification research, with several researchers proposing that gamified systems should take personal characteristics into account. However, creating good gamified designs is effort intensive as it is and tailoring system interactions to each user will only add to this workload. We propose machine learning algorithm -based personalized content selection to address a part of this problem and present a process for creating personalized designs that allows automating a part of the implementation. The process is based on Deterding’s 2015 framework for gameful design, the lens of intrinsic skill atoms, with additional steps for selecting a personalization strategy and algorithm creation. We then demonstrate the process by implementing personalized gamification for a computer-supported collaborative learning environment. For this demonstration, we use the gamification user type hexad for personalization and the heuristics for effective design of gamification for overall design. The result of the applied design process is a context-aware, personalized gamification ruleset for collaborative environments. Lastly, we present a method for translating gamification rulesets to machine-readable classifier algorithm using the CN2 rule inducer.

Participatory sensing (PS) and citizen science hold promises for a genuinely interactive and inclusive citizen engagement in meaningful and sustained collection of data about social and environmental phenomena. Yet the underlying motivations for public engagement in PS remain still unclear particularly regarding the role of gamification, for which HCI research findings are often inconclusive. This paper reports the findings of an experimental study specifically designed to further understand the effects of gamification on citizen engagement. Our study involved the development and implementation of two versions (gamified and non-gamified) of a mobile application designed to capture lake ice coverage data in the sub-arctic region. Emerging findings indicate a statistically significant effect of gamification on participants' engagement levels in PS. The motivation, approach and results of our study are outlined and implications of the findings for future PS design are reflected.

Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area.

Testing and quality assurance are characterized as the most expensive tasks in the software life cycle. However, several studies also indicate that the industry could enhance product quality and reduce costs by investing in developing testing practices. Software engineering educators can bridge the gap between formal education and industry practices to produce more industry-ready graduates, by observing the industry in action. To find out the current state of industry, we conducted a study in software organizations to assess how they test their products and which process models they follow. According to the survey results, the organizations rely heavily on test automation and use sophisticated testing infrastructures, apply agile practices even when working with mission-critical software, and have reduced the use of formal process reference and assessment models. Based on the results, this paper identifies a number of key learning objectives in quality assurance and software testing disciplines that the industry expects from university graduates. The principles of constructive alignment are used to present learning goals, teaching methods, and assessment methods that align with the industry requirements.

Currently there is an issue in the design process of smart city services, where citizens as the main stakeholders are not involved enough in requirements engineering.In this paper, we present a meta-level design science process, based on an extended version of design science research methodology, that can be used to create requirements engineering frameworks to inform smart city service requirements engineering processes.The introduced meta-level process is beneficial as it can be used to ensure that design guideline research processes are rigorous, just as design science process ensures scientific rigor in design research.Additionally, we present a previous case study and frame it using the new meta-level design science process.

Software systems have established themselves as the heart of business and everyday living, and as the pillar of the emerging global digital economy. This puts pressure on educational institutions to train people for the continuously evolving software industry, which puts additional demand for new PhDs and educators. While large universities and research centres have the required expertise and infrastructure to providing a cost-effective training by research as well as covering wide spectrum of software engineering topics, the situation in small universities with limited resources is challenging. This is even more difficult for some countries where the discipline of software engineering is totally new, which is the case of emerging countries. This paper describes the Pathways to PhDs project funded by the European Commission. The long-term aim is to support the development, modernization and international visibility and excellence of higher education, namely education by research at the PhD level in Europe, while helping partner countries to develop new PhD programs and consolidate existing ones in the field of computing in the area of software engineering. This paper presents the creation of a common educational framework that consist of seven specialized short-intensive schools, each of them feature the expertise and "savoir-faire" of participating universities. The collaboration and communication among partners and the schools is supported by an innovative communication platform, which is also presented in the paper.

In this paper, an approach and a rubric for measuring commitment to computer-supported collaboration in student teams is presented.

A key challenge for computer science educators worldwide is providing effective feedback and support to students, to ensure they are engaged with the course. This includes online feedback on discussion forums as well as feedback on programming assignments. Due to the significant problems of scale that need to be addressed, effective lecturer intervention is difficult, and at the same time the effect of intervention in online discussion forums is challenging to measure accurately. The same problem occurs when marking programming assignments, where detailed, in-depth feedback is often replaced with output from failed testcases, which the students sometimes proceed to address without giving thought to the quality of their overall solutions.