Requirements engineering (RE) involves human-centric activities that require interaction among different stakeholders. Traditionally, RE has been considered as a centralized, collocated, and phase-specific process. However, in open source software (OSS) development environments, the core RE activities are iterative and dynamic and follow a rather decentralized software engineering paradigm. This crosscutting characteristic of open source RE can be conceptualized using the “Twin Peaks” model that weaves RE together with software architecture. Although many weaving mechanisms have been proposed in recent years, the lack of theoretical underpinning limits a mechanism’s applicability and usefulness in different scenarios. In this research, we hypothesize stakeholders’ social interaction as an ecologically valid weaving mechanism of the “Twin Peaks” in open source RE. In this work, we use the phrase “stakeholders’ social interaction” to indicate interaction among stakeholders regarding the software system that takes place through some communication means, such as posting comments and artifacts over the issue tracking system. We investigate the influence of stakeholders’ social interaction in different RE activities, in particular, requirements identification, creativity in RE, and requirements implementation of OSS systems. This research enables us to gain valuable insights to generate guidelines for enhancing software engineering practice in relevant areas.

Software intensive systems are developed to provide solutions in some problem domain and software engineering principles are employed to develop and implement that system. Software engineering principles should enhance the development and production of software artifacts and yet the artifacts often lack in quality. Crucial in the development process are requirements engineering activities and methods for software documentation. This research focused on requirements engineering activities, software requirements documentation and employed a new approach in these activities that incorporated ontology engineering principles. Ontology engineering refers to the set of activities concerned with the ontology development process, the ontology life cycle, the methods for building ontologies, and the tool suites and languages that support them. Ontologies facilitate domain knowledge reuse and sharing and provides a common vocabulary to system developers. The motivation of this research came from Ambr´osio and Kaiya, advocating the definition of the Software Requirements Knowledge Area of the Software Engineering Body of Knowledge (SWEBOK ) within an ontology system. The resulting system utilized the benefits of intelligent reasoning to elicit, automatically verify, extract and document software requirements. The requirements engineering process was modeled in an ontology. An ontology is a machine-readable data structure that distinctly defines concepts and describes relationships among those concepts. The requirements engineering process and ontology were the focal points in this research. A baseline ontology for software requirements engineering was created. The following are contributions of this research. A methodology was designed to enhance the software documentation production process. An initial ontology model of SWEBOK recommended data items was created. A method was provided to verify software requirements as they were elicited, entered and maintained in an ontology. A method was created that electronically provided provenance of software requirements. Software was created to automatically extract the software requirements from within an ontology.

The field of software engineering consists of complex processes to deliver valuableand useful software to end users. Requirements discovery and software testing hasevolved significantly over the last decades with an increased focus on agility anddelivering customer value. Behavior-Driven Development (BDD), an extension ofTest-Driven Development, is a test-first requirements collection and acceptance test-ing framework. Despite a high practitioner interest within the industry, there arecurrently only a limited number of studies within academia available on the feasibilityof BDD. The aim of this thesis is to investigate the impact of BDD on software qualityand stakeholder collaboration. This is done by studying a quality assurance teamconsisting of management and development resources as BDD activities are practicedin the development of a new application. Semi-structured interviews are then heldwith participants to identify perceived and expected benefits as well as identifiedchallenges throughout the process. Responses are finally collected and coded into athematic map from which conclusions are drawn and discussed. As we have found in our study, many practical and organizational aspects areraised when BDD is implemented. Benefits include increased team collaboration,team alignment, and software quality. Challenges include management and teammotivation issues, increased workload, loss of productivity, BDD benefit visibilityissues, and the need for experience to be able to implement BDD successfully.

The success of software development projects heavily depends on effectively capturing and meeting stakeholders' requirements. However, involving multiple stakeholders with diverse backgrounds and objectives often leads to conflicts among these requirements. These conflicts represent inconsistencies in the system design, resulting in various challenges, including project delays, increased costs, and potential system failures. Previous research has primarily focused on identifying conflicts with algorithms or negotiation, while conversational AI's potential to detect conflicts in real-time has been neglected. This thesis study addresses the challenge of requirement conflicts by proposing a novel approach that leverages conversational AI in the form of a chatbot. The chatbot, developed using the Rasa platform, enables real-time detection of conflicts, focusing on three general types: duplicated (similar), incompatible, and contradictory requirements. During the study, the design science research method is employed to guide the chatbot's development. Further, an experiment is applied to evaluate the chatbot's performance compared with domain experts using four different datasets. The experiment results are presented using F1 scores, which calculate precision and recall for both the chatbot and the experts on each dataset. Overall, the chatbot scored 0.8, while the experts achieved a slightly higher score of 0.86. To determine if there was a statistically significant difference between the two performances, a Wilcoxon signed-rank test was conducted on the results. The analysis showed no significant difference in the F1 score between the chatbot and the experts, indicating the chatbot's feasibility and effectiveness in detecting conflicts. The contribution of this thesis study can advance requirements engineering by providing a user-friendly and efficient method for real-time conflict detection, enhancing the quality and overall success of software development projects.

This thesis was conducted by two students as part of their Strategic Information Systems Management degree program at Stockholm University. As presented in this study, the manual elicitation processes in Requirements Engineering are error-prone and time-consuming. Traditional approaches and techniques often produce requirements that are characterized by ambiguity, inadequacy, incompleteness, inconsistency, and obsolescence. The research problem is focused on the lack of clear understanding regarding AI's specific role in supporting the identification of precise and detailed requirements, and the need for summarizing findings of related work. The goal of this thesis is to investigate the impact of AI in Requirements Engineering, focusing primarily on Requirements Elicitation and Analysis. After presenting essential background knowledge of Requirements Engineering, traditional elicitation methods, and Artificial Intelligence, a systematic literature review was performed to unveil Artificial Intelligence methods, techniques, and tools used in Requirements Elicitation and Analysis. With the assistance of the PRISMA methodology, the key findings and results were summarized and presented. The majority of the online literature focused on various issues connected with traditional methods and presented how Artificial Intelligence Chatbots, Text Mining and Natural Language Processing techniques, Virtual Reality, Sentiment Analysis, Crowdsourcing, Deep Learning Techniques, Gamification, and Bayesian Networks are improving the quality and speed of Requirements Elicitation. One of the main challenges faced is that there is no extensive comparison with traditional methods and metrics on how Artificial Intelligence overall helps Requirements Elicitation – only metrics per case. Also, there is not explicit definition regarding which AI methodologies and tools are appropriate for each elicitation and analysis method.

Fueled by the success of cryptocurrencies such as Bitcoin, blockchain technology has emerged as an interesting and promising technological solution in a variety of industries. However, it remains unclear to what extent blockchain technology possesses the potential to transform existing business models or to enable new business models. So far, scientific studies have been predominantly technologically driven; the economic viability, e.g., in the form of innovative, sustainable business models, as well as the acceptance of blockchain technology have not been sufficiently investigated. This dissertation addresses this research gap and focuses on three domains: The financial sector, the temporary employment industry, and the healthcare sector. To investigate the impact of blockchain technology on business model innovation as well as its impact on existing business models, quantitative and qualitative methods are applied in the form of a mixed-method approach. Within this approach, (acceptance) models, tools, methods, and prototypes are developed. Moreover, recommendations for decision makers are elaborated and central economic, ecological, political, legal, social, and ethical challenges and opportunities of blockchain technology for business models and business model innovations are identified and discussed. The results of this dissertation support economic and political decision makers as well as researchers in the fields of blockchain technology and business model innovation.

Despite significant efforts at improving requirements engineering in the development of military systems, defense procurement is still plagued by expensive, well-publicized failures. Central to requirements engineering is the concept of rationality – more reason is assumed to eventually ‘solve’ the problem of defense requirements engineering. This thesis suggests that rationality, instead of being an objective standard, might be part of a socially constructed framework for action. Leaning on Science and Technology Studies for a theoretical framework, it is suggested that rationality and irrationality is part of a larger sociotechnical imaginary which outlines desirable outcomes, actions, and values in military systems development. This thesis presents an interview study of requirements analysts in the Swedish defense sector to outline if and how rationality relates to the narrative of this potential imaginary. The results indicate that a Swedish defense requirements engineering imaginary consists of a rationality/irrationality dichotomy which sets the stage for action in a state of chaos, and that the narrative associated with that imaginary enables the

This thesis aims to study the use of Critical System Heuristic (CSH) in the requirements engineering (RE) process of a student software project. We have studied a software project within the framework of the TDDD96 course at Linköping University. The project consisted of a group of computer science students working with a representative from a company. As part of the course, the students had done the sustainability exercise SusAF, in which they evaluated their project based on multiple sustainability metrics. We have conducted one round of interviews with the RE student, the company representative, and an expert in the area. The answers were encoded and mapped to 12 CSH questions before being presented in the Ideal map table. The results produced by CSH show the many benefits of integrating CSH into the course. We have proposed different ways of integrating CSH with the SusAF exercise. From the result, we realized the importance of consulting a third party that could provide an outside perspective on different issues. However, an essential aspect of using CSH is to consult the appropriate party. To this end, we found that CSH could be used internally to point in the right direction.

Increasingly competitive software industry, where multiple systems serve the same application domain and compete for customers, favors software with creative features. To promote software creativity, research has proposed multi-day workshops with experienced facilitators, and semi-automated tools to provide a limited support for creative thinking. Such approach is either time consuming and demands substantial involvement from analysts with creative abilities, or useful only for existing large-scale software with a rich issue tracking system. In this dissertation, we present different approaches leveraging advanced natural language processing and machine learning techniques to provide automated support for generating creative software requirements with minimal human intervention. A controlled experiment is conducted to assess the effectiveness of our automated framework compared to the traditional brainstorming technique. The results demonstrate our frame-work’s ability to generate creative features for a wide range of stakeholders and provoke innovative thinking among developers with various experience levels.

[pt] Sistemas que usam Aprendizado de Máquina, doravante Machine Learning (ML), tornaram-se comuns para empresas que deseajam melhorar seus produtos, serviços e processos. A literatura sugere que a Engenharia de Requisitos (ER) pode ajudar a explicar muitos problemas relacionados à engenharia de sistemas inteligentes envolvendo componentes de ML (ML-Enabled Systems). Contudo, o cenário atual de evidências empíricas sobre como ER é aplicado na prática no contexto desses sistemas é amplamente dominado por estudos de casos isolados com pouca generalização. Nós conduzimos um survey internacional para coletar informações de profissionais sobre o status quo e problemas de ER para ML-Enabled Systems. Coletamos 188 respostas completas de 25 países. Realizamos uma análise quantitativa sobre as práticas atuais utilizando bootstrapping com intervalos de confiança; e análises qualitativas sobre os problemas reportados através de procedimentos de codificação open e axial. Encontramos diferenças significativas nas práticas de ER no contexto de projetos de ML, algumas já reportadas na literatura e outras totalmente novas. Por exemplo, (i) atividades relacionadas à ER são predominantemente conduzidas por líderes de projeto e cientistas de dados, (ii) o formato de documentação predominante é baseado em Notebooks interativos, (iii) os principais requisitos não-funcionais incluem qualidade dos dados, confiança e explicabilidade no modelo, e (iv) os principais desafios consistem em gerenciar a expectativa dos clientes e alinhar requisitos com os dados disponíveis. As análises qualitativas revelaram que os praticantes enfrentam problemas relacionados ao baixo entendimento sobre o domínio do negócio, requisitos pouco claros e baixo engajamento do cliente. Estes resultados ajudam a melhorar o entendimento sobre práticas adotadas e problemas existentes em cenários reais. Destacamos a necessidade para adaptar ainda mais e disseminar práticas de ER relacionadas à engenharia de ML-Enabled Systems. / [en] Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products, services, and processes. Literature suggests that Requirements Engineering (RE) can help to address many problems when engineering ML-Enabled Systems. However, the state of empirical evidence on how RE is applied in practice in the context of MLenabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects, some of them have been reported on literature and some are totally new. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear requirements, and low customer engagement. These results help to provide a better understanding of the adopted practices and which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.