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This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

Introduction: It is necessary to create interactive and innovative spaces that guide nursing students learning toward the integration of knowledge and skills for carrying out a basic care process. Objective: To design an application software as an educational tool to develop the secretion aspiration competency of nursing students. Methods: Qualitative research with an evaluative design for a software application validated by nine students, nine teachers, a graduate, and a technological expert. The research was developed in three stages: application software design, software assessment, and creation of didactic sequences. Results: A table of competencies for teaching the secretion aspiration procedure was designed according to the syllabus; based on this, the script for the design of the software was prepared, which has four units: three for reviewing and reinforcing previous knowledge, and the unit for the nursing procedure. Three didactic sequences for using the software in face-to-face, online, and autonomous learning environments were created, as well as an evaluation matrix for the simulation process at the laboratory. Conclusions: The design of this application software is suitable for carrying out teaching-learning processes aimed at developing cognitive, procedural, and attitudinal competencies. In addition, the creation of this type of tool by the nursing program faculty ensures consistency with the pedagogical model of the institution and the academic program guidelines., Introdução: é necessário gerar espaços interativos e inovadores que orientem o aprendizado dos discentes de enfermagem para integrar conhecimentos e habilidades na execução de um processo de cuidado básico. Objetivo: projetar um software tipo aplicativo como ferramenta educacional para desenvolver a competência de aspiração de secreções em discentes de enfermagem. Métodos: pesquisa qualitativa com desenho avaliativo para um software validado por nove alunos, nove professores, um graduado e um especialista tecnológico, foi desenvolvido em três momentos: design do software aplicativo, avaliação do software e geração de sequências didáticas. Resultados: organizou-se um quadro de competências para o ensino do procedimento de aspiração de secreções de acordo com a ementa correspondente; A partir disso, elaborou-se o roteiro para o design do software, que possui quatro unidades: três para revisão e consolidação do conhecimento prévio, e a unidade do referido procedimento de enfermagem. Foram construídas três sequências didáticas para utilização do software nos ambientes de aprendizagem: presencial, virtual e autônomo, além de uma matriz de avaliação para o processo de simulação em laboratório. Conclusões: o design do software aplicativo foi oportuno para realizar processos de ensino-aprendizagem voltados para o desenvolvimento de habilidades cognitivas, processuais e atitudinais. Além disso, a geração desse tipo de ferramenta pelos docentes do curso de enfermagem garante coerência com o modelo pedagógico institucional e as diretrizes do curso acadêmico., Introducción: es necesario generar espacios interactivos e innovadores que orienten el aprendizaje de los estudiantes de enfermería para integrar conocimientos y habilidades en la ejecución de un proceso de cuidado básico. Objetivo: diseñar un software de aplicación como herramienta educativa para desarrollar la competencia de aspiración de secreciones en estudiantes de enfermería. Métodos: investigación cualitativa con un diseño evaluativo para un software, validado por nueve estudiantes, nueve docentes, un egresado y un experto tecnológico, el cual se desarrolló en tres momentos: diseño del software de aplicación, evaluación del software y generación de secuencias didácticas. Resultados: se organizó una tabla de competencias para la enseñanza del procedimiento de aspiración de secreciones acorde con los syllabus correspondientes; a partir de esta, se elaboró el guion para el diseño del software, el cual cuenta con cuatro unidades: tres de repaso y afianzamiento de saberes previos, y la unidad del procedimiento de enfermería mencionado. Se construyeron tres secuencias didácticas para el uso del software en los ambientes de aprendizaje: presencial, virtual y autónomo, así como una matriz de evaluación para el proceso de simulación en el laboratorio. Conclusiones: el diseño de este software de aplicación resulta oportuno para llevar a cabo procesos de enseñanza-aprendizaje orientados al desarrollo de competencias cognitivas, procedimentales y actitudinales. Además, la generación de este tipo de herramientas por parte de los docentes del programa de enfermería garantiza la coherencia con el modelo pedagógico institucional y los lineamientos del programa académico.

Presenting the developments of my practice based research into using creative coding to generate digital variable print, focusing on the impacts and findings relating to using this approach in both my design and teaching practice.

A talk and workshop communicating new approaches to variable digital print through the use of creative code and its potential impact on publishing. The workshop used analogue activities to understand how creative coding works in order to generate variable outcomes.

Seeking the advantages delivered by agile methods in small-scale software development, large organisations are also adopting agile methods. However, scaling results in a huge growth of socio-technical dependencies that can lead to waiting time, delays, and defects and hinder the teams' ability to recognize their own responsibilities. This research proposes an approach to enable teams' autonomy and clarifies teams' responsibility assignments by aligning socio-technical dependencies. By utilising compile-time, run-time, and task dependencies, our approach identifies the wasteful dependencies between the social structures (teams) and the corresponding technical structures (architecture) and also suggests improvements. The initial results suggest that the approach correctly identifies the wasteful dependencies that are hindering teams' responsibility assignments. The suggested solution proposals are also considered useful. Awareness of such wasteful dependencies is the first step toward being able to handle them successfully. © 2023 IEEE.

Employing an agile development methodology, particularly Scrum, in a distributed student project setting is challenging for both teachers and involved students. Allowing distributed student teams to self-organize and assign key Scrum roles using various strategies, specifically regarding the locations of students taking on key roles, increases the complexity of such projects. In addition, the interaction of the Project Owner role with the project customer, which occurs outside the distributed student team, adds a new dimension to this problem. This paper investigates the impact of various key role assignment strategies, and their interactions, on the performance of distributed student projects. Furthermore, we investigate the intensity of collaboration within the distributed team and between key project roles, as well as their impact on project performance. We analyzed data collected on 37 distributed student projects conducted over the course of eight academic years. The results reveal that letting students assign key project roles regardless of their location in the distributed team has no significant impact on the quality of project outcomes. However, a deeper analysis uncovers that more educationally desirable assignments of those roles exist; favoring increased collaboration intensity within distributed student teams.

HERO-ML is an array language, on very high level, which is intended for specifying data parallel algorithms in a concise and platform-independent way where all the inherent data parallelism is easy to identify. The goal is to support the software development for heterogeneous systems with different kinds of parallel numerical accelerators, where programs tend to be very platform-specific and difficult to develop. In this paper we describe HERO-ML, and a proof-of-concept implementation.

During the last three decades, automation in software development has gone mainstream. Software development teams strive to automate as much of the software development activ-ities as possible, spanning requirements specification, system modeling, code generation, testing, deployment, verification, as well as release phases, project status reporting and system maintenance. Automation helps to reduce development time and cost, as well as to concentrate know ledge by bringing quality into every step of the development process.

Seeking the advantages delivered by agile methods in small-scale software development, large organisations are also adopting agile methods. However, scaling results in a huge growth of socio-technical dependencies that can lead to waiting time, delays, and defects and hinder the teams' ability to recognize their own responsibilities. This research proposes an approach to enable teams' autonomy and clarifies teams' responsibility assignments by aligning socio-technical dependencies. By utilising compile-time, run-time, and task dependencies, our approach identifies the wasteful dependencies between the social structures (teams) and the corresponding technical structures (architecture) and also suggests improvements. The initial results suggest that the approach correctly identifies the wasteful dependencies that are hindering teams' responsibility assignments. The suggested solution proposals are also considered useful. Awareness of such wasteful dependencies is the first step toward being able to handle them successfully. © 2023 IEEE.

Context: Non-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited. Objective: This study examines how automated NFR testing can be enabled and supported using CI environments in software development companies. Method: We performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners. Results: Maintainability, reliability, performance, security and scalability, were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported. Conclusions: This study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice. © 2023, The Author(s).

Context: Non-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited. Objective: This study examines how automated NFR testing can be enabled and supported using CI environments in software development companies. Method: We performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners. Results: Maintainability, reliability, performance, security and scalability, were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported. Conclusions: This study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice. © 2023, The Author(s).

Context: Non-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited. Objective: This study examines how automated NFR testing can be enabled and supported using CI environments in software development companies. Method: We performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners. Results: Maintainability, reliability, performance, security and scalability, were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported. Conclusions: This study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice. © 2023, The Author(s).

Seeking the advantages delivered by agile methods in small-scale software development, large organisations are also adopting agile methods. However, scaling results in a huge growth of socio-technical dependencies that can lead to waiting time, delays, and defects and hinder the teams' ability to recognize their own responsibilities. This research proposes an approach to enable teams' autonomy and clarifies teams' responsibility assignments by aligning socio-technical dependencies. By utilising compile-time, run-time, and task dependencies, our approach identifies the wasteful dependencies between the social structures (teams) and the corresponding technical structures (architecture) and also suggests improvements. The initial results suggest that the approach correctly identifies the wasteful dependencies that are hindering teams' responsibility assignments. The suggested solution proposals are also considered useful. Awareness of such wasteful dependencies is the first step toward being able to handle them successfully. © 2023 IEEE.

Context: Non-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited. Objective: This study examines how automated NFR testing can be enabled and supported using CI environments in software development companies. Method: We performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners. Results: Maintainability, reliability, performance, security and scalability, were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported. Conclusions: This study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice. © 2023, The Author(s).

Seeking the advantages delivered by agile methods in small-scale software development, large organisations are also adopting agile methods. However, scaling results in a huge growth of socio-technical dependencies that can lead to waiting time, delays, and defects and hinder the teams' ability to recognize their own responsibilities. This research proposes an approach to enable teams' autonomy and clarifies teams' responsibility assignments by aligning socio-technical dependencies. By utilising compile-time, run-time, and task dependencies, our approach identifies the wasteful dependencies between the social structures (teams) and the corresponding technical structures (architecture) and also suggests improvements. The initial results suggest that the approach correctly identifies the wasteful dependencies that are hindering teams' responsibility assignments. The suggested solution proposals are also considered useful. Awareness of such wasteful dependencies is the first step toward being able to handle them successfully. © 2023 IEEE.

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

This paper describes the art installation of Kiss/Crash: a multi-screen work exploring the subject of AI-imagery and representation as well as the autobiographical themes of loneliness, desire, and intimacy in the digital age. The installation consists of three individual works in a shared space, Kiss/Crash, Me Kissing Me, and Crash Me, Gently, which all play with augmenting, inverting, and negating the iconic image of the kiss using diffusion-based image translation. In the production of this work, several potentially novel video translation techniques were developed and refined to create high-quality results central to these pieces. Throughout our research, we reflect on the nature of images and place diffusion models within a history of image-production technologies that artists have been contending with and responding to for the past one hundred years. This paper aims to extend that artistic tradition with a provocative, original aesthetic and technique that reveals the logic of AI imagery and hints at how our relationship to reality will continue to be stretched and shaped by artificial representations at an accelerating pace.

Modern software development is a team-based effort supported by tools, processes, and practices. One integral part is automated testing, where developers incorporate automated tests on multiple levels of system abstraction, from low-level unit tests to high-level system tests and Graphical User Interface (GUI) tests. Furthermore, the common practices of code reviews allow collaboration on artifacts based on discussions that improve the artifact's quality and to share information within the team. However, the characteristics of GUI-based tests, due to the level of abstraction and visual elements, introduce additional requirements and complexities compared to code or lower-level test code review, delimiting the practice benefits.The objective of this work is to propose a tool-supported workflow that enables active collaboration among stakeholders and improves the efficiency and effectiveness of team-based development of GUI-based tests.To evaluate the workflow, and show proof of concept, a technical demonstrator for merging of GUI-based tests was to be developed. However, during its development, we encountered several unforeseen challenges that forced us to halt its development. We report the negative results from this development and the main challenges we encountered, as well as the rationale and the decisions we took towards this workflow.In conclusion, this work presents a negative research result on a failed attempt to propose a tool-supported workflow that enables active collaboration on GUI-based tests. The outcome and learnings of this work are intended to guide future research and prevent researchers from falling into the same pitfalls we did. © 2023 IEEE.

Context: Bug reports created during software development and maintenance do not always describe deviations from a system's valid behavior. Such invalid bug reports may consume significant resources and adversely affect the prioritization and resolution of valid bug reports. There is a need to identify preventive actions to reduce the inflow of invalid bug reports. Existing research has shown that manually analyzing invalid bug report descriptions provides cues regarding preventive actions. However, such a manual approach is not cost-effective due to the time required to analyze a sufficiently large number of bug reports needed to identify useful patterns. Furthermore, the analysis needs to be repeated as the underlying causes of invalid bug reports change over time. Objective: In this study, we propose and evaluate the use of Latent Dirichlet Allocation (LDA), a topic modeling approach, to support practitioners in suggesting preventive actions to avoid the creation of similar invalid bug reports in the future. Method: In an industrial case study, we first manually analyzed descriptions of invalid bug reports to identify common patterns in their descriptions. We further investigated to what extent LDA can support this manual process. We used expert-based validation to evaluate the relevance of identified common patterns and their usefulness in suggesting preventive measures. Results: We found that invalid bug reports have common patterns that are perceived as relevant, and they can be used to devise preventive measures. Furthermore, the identification of common patterns can be supported with automation. Conclusion: Using LDA, practitioners can effectively identify representative groups of bug reports (i.e., relevant common patterns) from a large number of bug reports and analyze them further to devise preventive measures. © 2023 The Author(s)

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap. © 2023, The Author(s).

Digital technology is now pervasive, however, not all groups have uniformly benefitted from technological changes and some groups have been left behind or digitally excluded. Comprehensive data from the 2017 Current Population Survey shows that older people and persons with disabilities still lag behind in computer and internet access. Furthermore unique ethical, privacy and safety implications exist for the use of technology for older persons and people with disabilities and careful reflection is required to incorporate these aspects, which are not always part of a traditional software lifecycle. In this paper we present the Inclusion4EU project that aims to co-design a new framework, guidelines and checklists for inclusive software design and development with end-users from excluded categories, academics with expertise in human-computer interaction and industry practitioners from software engineering.

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

Well-trained software development personnel, in the art and science of software testing, will effectively and efficiently develop quality software products with potentially fewer, less-critical defects. Thus software testing education is considered to be an important part of curricula for a university degree in Computer Science or Information Systems. The objective of this paper is to determine how much dedicated knowledge in the field of software testing is taught within Swedish universities. To achieve this objective, a systematic search of syllabi for software testing-related courses was done. From 25 Swedish universities offering Computer Science (or related) degrees, 14 currently offer dedicated courses in software testing. Some findings include: 32% of the individual courses were offered at the undergraduate level; 28% of the universities offer courses for specialised testing training; and, for the vast majority of the universities, dedicated software testing courses account for about 5% of the total degree credits. While some universities fare better than others, the overall state of academic software testing education in Sweden is limited but promising.

Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations.This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving., This project has received funding from Vinnova Swedenunder the FFI program with grant agreement No 2021-02572(precog), from the EU’s Horizon 2020 research and innovationprogram under grant agreement No 957197 (vedliot), and froma Swedish Research Council (VR) Project: Non-FunctionalRequirements for Machine Learning: Facilitating ContinuousQuality Awareness (iNFoRM).

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools. Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE. © 2023 The Authors.

While modern software companies strive to increase team autonomy to enable them to successfully operate the piece of software they develop and deploy, efficient ways to orchestrate the work of multiple autonomous teams working in parallel are still poorly understood. In this paper, we report how team autonomy is maintained at Spotify at scale, based on team retrospectives, interviews with team managers and archival analysis of corporate databases and work procedures. In particular, we describe how managerial authority is decentralized through various workgroups with collective authority, what compromises are made to team autonomy to ensure alignment and which team-related factors can further hinder autonomy. Our findings show that scaled autonomy at Spotify does not mean anarchy, or unlimited permissiveness. Instead, squads are expected to take responsibility for their work and coordinate, communicate and align their actions with others, and comply with a few enabling constraints. Further, squads take many decisions independently without management control or due to collective efforts that bypass formal boundary structures. Mechanisms and strategies that enable self-organization at Spotify are related to effective sharing of the codebase, achieving alignment, networking and knowledge sharing, and are described to guide other companies in their efforts to scale autonomy. © 2023 The Author(s)

Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

Context: Software selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns. Objective: Our main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment. Method: We used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company. Results: The model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools. Conclusions: We have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors. © 2023, The Author(s)., ELLIIT

During the last century, various countries' armed forces have used unmanned aerial vehicles, commonly known as drones. In recent years, strives have been made to develop small commercial drones that have allowed the general public to afford and use them for recreational purposes. The availability of drones has led to immoral applications of the technology. Such applications need to be faced with anti-measures and effective detection methods. Therefore, this thesis aims to develop a mobile reconnaissance robot that can detect commercial drones with radar. It describes integrating radar sensors with single-board computers to detect and localise air-bound objects. The finished product aims to be used for educational and exhibition purposes at the Swedish Armed Forces technical school to increase awareness of the technology., Försvarsmaktens tekniska skola i Halmstad var intressenter för uppsatsen.

Modern software development is a team-based effort supported by tools, processes, and practices. One integral part is automated testing, where developers incorporate automated tests on multiple levels of system abstraction, from low-level unit tests to high-level system tests and Graphical User Interface (GUI) tests. Furthermore, the common practices of code reviews allow collaboration on artifacts based on discussions that improve the artifact's quality and to share information within the team. However, the characteristics of GUI-based tests, due to the level of abstraction and visual elements, introduce additional requirements and complexities compared to code or lower-level test code review, delimiting the practice benefits.The objective of this work is to propose a tool-supported workflow that enables active collaboration among stakeholders and improves the efficiency and effectiveness of team-based development of GUI-based tests.To evaluate the workflow, and show proof of concept, a technical demonstrator for merging of GUI-based tests was to be developed. However, during its development, we encountered several unforeseen challenges that forced us to halt its development. We report the negative results from this development and the main challenges we encountered, as well as the rationale and the decisions we took towards this workflow.In conclusion, this work presents a negative research result on a failed attempt to propose a tool-supported workflow that enables active collaboration on GUI-based tests. The outcome and learnings of this work are intended to guide future research and prevent researchers from falling into the same pitfalls we did. © 2023 IEEE.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations.This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving., This project has received funding from Vinnova Swedenunder the FFI program with grant agreement No 2021-02572(precog), from the EU’s Horizon 2020 research and innovationprogram under grant agreement No 957197 (vedliot), and froma Swedish Research Council (VR) Project: Non-FunctionalRequirements for Machine Learning: Facilitating ContinuousQuality Awareness (iNFoRM).

Model-based test design is increasingly being applied in practice and studied in research. Model-based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT: (i) some studies did not fully describe how tools transform abstract tests into concrete tests, (ii) some studies overlooked the computational cost of model-based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments., © 2023 John Wiley & Sons Ltd.Correspondence: Fabiano C. Ferrari, Rodovia Washington Luis, Km 235, São Carlos, São Paulo - Brazil.Email: fcferrari@ufscar.brFabiano Ferrari was partly supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) - Brasil, grant #2016/21251-0 and CNPq - Brasil, grants #306310/2016-3 and #312086/2021-0. Sten Andler was partly supported by KKS (The Knowledge Foundation), by project 20130085, Testing of Critical System Characteristics (TOCSYC). Mehrdad Saadatmand was partly funded by the SmartDelta Project (more information available at https://smartdelta.org/).

Context: Bug reports created during software development and maintenance do not always describe deviations from a system's valid behavior. Such invalid bug reports may consume significant resources and adversely affect the prioritization and resolution of valid bug reports. There is a need to identify preventive actions to reduce the inflow of invalid bug reports. Existing research has shown that manually analyzing invalid bug report descriptions provides cues regarding preventive actions. However, such a manual approach is not cost-effective due to the time required to analyze a sufficiently large number of bug reports needed to identify useful patterns. Furthermore, the analysis needs to be repeated as the underlying causes of invalid bug reports change over time. Objective: In this study, we propose and evaluate the use of Latent Dirichlet Allocation (LDA), a topic modeling approach, to support practitioners in suggesting preventive actions to avoid the creation of similar invalid bug reports in the future. Method: In an industrial case study, we first manually analyzed descriptions of invalid bug reports to identify common patterns in their descriptions. We further investigated to what extent LDA can support this manual process. We used expert-based validation to evaluate the relevance of identified common patterns and their usefulness in suggesting preventive measures. Results: We found that invalid bug reports have common patterns that are perceived as relevant, and they can be used to devise preventive measures. Furthermore, the identification of common patterns can be supported with automation. Conclusion: Using LDA, practitioners can effectively identify representative groups of bug reports (i.e., relevant common patterns) from a large number of bug reports and analyze them further to devise preventive measures. © 2023 The Author(s)

High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap. © 2023, The Author(s).

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools. Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE. © 2023 The Authors.

Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

While modern software companies strive to increase team autonomy to enable them to successfully operate the piece of software they develop and deploy, efficient ways to orchestrate the work of multiple autonomous teams working in parallel are still poorly understood. In this paper, we report how team autonomy is maintained at Spotify at scale, based on team retrospectives, interviews with team managers and archival analysis of corporate databases and work procedures. In particular, we describe how managerial authority is decentralized through various workgroups with collective authority, what compromises are made to team autonomy to ensure alignment and which team-related factors can further hinder autonomy. Our findings show that scaled autonomy at Spotify does not mean anarchy, or unlimited permissiveness. Instead, squads are expected to take responsibility for their work and coordinate, communicate and align their actions with others, and comply with a few enabling constraints. Further, squads take many decisions independently without management control or due to collective efforts that bypass formal boundary structures. Mechanisms and strategies that enable self-organization at Spotify are related to effective sharing of the codebase, achieving alignment, networking and knowledge sharing, and are described to guide other companies in their efforts to scale autonomy. © 2023 The Author(s)

Context: Software selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns. Objective: Our main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment. Method: We used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company. Results: The model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools. Conclusions: We have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors. © 2023, The Author(s)., ELLIIT

High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap. © 2023, The Author(s).

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools. Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE. © 2023 The Authors.

Modern software development is a team-based effort supported by tools, processes, and practices. One integral part is automated testing, where developers incorporate automated tests on multiple levels of system abstraction, from low-level unit tests to high-level system tests and Graphical User Interface (GUI) tests. Furthermore, the common practices of code reviews allow collaboration on artifacts based on discussions that improve the artifact's quality and to share information within the team. However, the characteristics of GUI-based tests, due to the level of abstraction and visual elements, introduce additional requirements and complexities compared to code or lower-level test code review, delimiting the practice benefits.The objective of this work is to propose a tool-supported workflow that enables active collaboration among stakeholders and improves the efficiency and effectiveness of team-based development of GUI-based tests.To evaluate the workflow, and show proof of concept, a technical demonstrator for merging of GUI-based tests was to be developed. However, during its development, we encountered several unforeseen challenges that forced us to halt its development. We report the negative results from this development and the main challenges we encountered, as well as the rationale and the decisions we took towards this workflow.In conclusion, this work presents a negative research result on a failed attempt to propose a tool-supported workflow that enables active collaboration on GUI-based tests. The outcome and learnings of this work are intended to guide future research and prevent researchers from falling into the same pitfalls we did. © 2023 IEEE.

Context: Bug reports created during software development and maintenance do not always describe deviations from a system's valid behavior. Such invalid bug reports may consume significant resources and adversely affect the prioritization and resolution of valid bug reports. There is a need to identify preventive actions to reduce the inflow of invalid bug reports. Existing research has shown that manually analyzing invalid bug report descriptions provides cues regarding preventive actions. However, such a manual approach is not cost-effective due to the time required to analyze a sufficiently large number of bug reports needed to identify useful patterns. Furthermore, the analysis needs to be repeated as the underlying causes of invalid bug reports change over time. Objective: In this study, we propose and evaluate the use of Latent Dirichlet Allocation (LDA), a topic modeling approach, to support practitioners in suggesting preventive actions to avoid the creation of similar invalid bug reports in the future. Method: In an industrial case study, we first manually analyzed descriptions of invalid bug reports to identify common patterns in their descriptions. We further investigated to what extent LDA can support this manual process. We used expert-based validation to evaluate the relevance of identified common patterns and their usefulness in suggesting preventive measures. Results: We found that invalid bug reports have common patterns that are perceived as relevant, and they can be used to devise preventive measures. Furthermore, the identification of common patterns can be supported with automation. Conclusion: Using LDA, practitioners can effectively identify representative groups of bug reports (i.e., relevant common patterns) from a large number of bug reports and analyze them further to devise preventive measures. © 2023 The Author(s)

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523. © 2023 IEEE.