Emotions play a critical role in shaping user experiences with interactive technologies, yet the HCI field has lacked a comprehensive, validated framework that captures the full spectrum of user affective states. This study addresses that gap by developing a PAD (Pleasure–Arousal–Dominance) model–based taxonomy specifically designed for human–computer interaction. Using a rigorous multi-stage methodology, the research synthesized findings from a systematic mapping of over 100 user emotions in the HCI literature, identifying 21 core emotional states based on frequency, relevance, and generalizability. A Delphi expert review refined the set, while each emotion was assigned PAD dimensional values ranging from –1 to 1. User studies in healthcare and gaming contexts validated the taxonomy’s ability to represent emotional trajectories, with new transitions informing iterative improvements. By integrating data-driven synthesis, expert consensus, and user-centered evaluation, this research delivers an evidence-based taxonomy to guide the engineering of emotionally aware interactive systems.

ABSTRACT Cybersecurity is a critical component of digital transformation, as the two must be integrated to enable business modernization and the adoption of innovative technologies while ensuring system security. Modern technologies introduce increasingly complex threats as more data is stored, transmitted, and processed across systems. Threats include the violation or misuse of data, such as data corruption, data theft, or a privacy violation. Determining if an organization applies the best practices requires measurements using maturity models. Several maturity models with different goals and qualities have been developed. However, no cybersecurity maturity model is related to digitally enabled or transforming organizations. The objective of this study is to develop a model that measures cybersecurity maturity for digitally transformed organizations. The model aims to provide a customizable assessment method for organizations of different sizes and domains. Two multivocal literature reviews (MLRs) were conducted to identify the available maturity models and best practices from formal and grey literature. After analyzing 165 studies, seven cybersecurity categories, as the capability areas, and 22 practice areas, were identified. Moreover, an assessment tool that supports self‐assessment was developed. Finally, the model was evaluated through five case studies and expert judgments. As a result, a cybersecurity maturity model for digitally transformed organizations is developed, along with its assessment methodology and automation tool. The model evaluation showed promising results in terms of the ability to identify the maturity level.

ABSTRACT Context Prioritizing usability in software development is vital to ensure a positive user experience and efficient performance. Failing to do so can result in negative consequences such as increased costs, damage to reputation, and loss of trust. Usability should be considered throughout the entire software development life cycle (SDLC), not just as an afterthought. Accordingly, adopting and integrating usability practices is crucial but can be complex. Thus, organizations must assess their readiness to start and improve usability initiatives. In this context, software organizations can benefit from using a usability readiness model to enhance integration and assist practitioners in systematically and structurally applying usability practices. Objective We propose a readiness model for usable software development (RMUSD). This model allows organizations to assess their readiness to create usable software, leading to improved user experience and better usability methods throughout the SDLC. Method To develop the RMUSD, we conducted a systematic mapping study that gathered 238 relevant studies in the usability engineering domain. From these studies, we synthesized 50 best usability practices that form the foundation of the RMUSD. We also drew inspiration from existing software engineering readiness and usability maturity models. Results The RMUSD is built at five levels, each with various aspects to consider, such as related usability practices, implications, organizational characteristics, and success factors for moving up to the next level. The model was evaluated through two real‐world case studies and eight experts. Conclusion The evaluation results reveal that the suggested RMUSD is practical in determining the organization's readiness to develop usable software.

Machine learning (ML) tools with graphical user interfaces (GUI) are facing demand from novice users who do not have the background of their underlying concepts. These tools are frequently complex and pose unique challenges in terms of interaction and comprehension by novice users. There is yet to be an established set of usability heuristics to guide and assess GUI ML tool design. To address this gap, in this paper, we extend Nielsen’s heuristics for evaluating GUI ML Tools through a set of empirical evaluations. To validate the proposed heuristics, user testing was conducted by novice users on a prototype that reflects those heuristics. Based on the results of the evaluations, our new heuristics set improves upon existing heuristics in the context of ML tools. It can serve as a resource for practitioners designing and evaluating these tools.

Code smells tend to have an impact on software quality attributes such as reusability, maintainability, and understandability. These are code flaws that do not necessarily prevent the system from operating; rather, they increase the possibility of defects occurring in the future. Hence, to maintain code quality, code smells should be detected and corrected through refactoring. The objective of this paper is to investigate the associated risk of applying refactoring techniques and reveal the bad smells that may appear when fixing other bad smells. We conducted several controlled experiments to identify the smells that emerge after refactoring. The experiments resulted in a novel taxonomy revealing 9 bad smells that may appear as a result of fixing other bad smells. This represents the first study attempting to systematically identify and organize such refactoring-smell relationships into a taxonomy. The research results can assist developers in relating different bad smells to each other and helping them determine the refactoring technique that should be applied when certain smells are present. Moreover, the results can be beneficial in breaking the cycle of bad smells. Such knowledge tends to enhance code refactoring, which in turn will improve software quality and avoid technical debt.

The Systems Modeling Language (SysML) is a popular and standardized UML profile for systems engineering applications. In addition to seven conventional UML diagram types, SysML supports requirement diagrams and tables that can be used to capture requirements, their attributes, and their relationships. However, several important concepts such as goals and contributions are not predefined in SysML, hindering the reasoning about tradeoffs and adaptation, especially in emerging socio-cyber-physical systems such as smart cities. In this paper, we provide a preliminary investigation of different ways of combining SysML with the User Requirements Notation (URN), a standard that focuses on the modeling and analysis of goals and scenarios. We argue that SysML and URN are complementary and synergistic, and that their combination enables new requirements modeling, analysis, and management opportunities for new types of systems. Examples of potential integration approaches are discussed, briefly assessed, and illustrated with existing tools for SysML, URN, and requirements management.