Competitive environments, whether in traditional sports or competitive online gaming, often foster intense emotions and harsh language. In Competitive Online Multiplayer Games, where players are not physically present together, verbal toxicity primarily manifests through voice and text chat. As online gaming remains a predominantly male-dominated space, much of this toxicity disproportionately targets non-male players. Behind the veil of anonymity, non-male players frequently face gender discrimination, hate speech, and unwanted sexual advances. To effectively address these issues, game developers must first understand how such harassment manifests and how it is perceived by players. This work examines gender discrimination in Competitive Online Multiplayer Games through two online surveys. The first survey gathered reports from 61 non-male players who had experienced harassment, resulting in 171 coded statements describing gender discrimination. A second survey presented these statements to 281 players across all genders, who rated their perceived severity and authenticity. Our results indicate that gender discrimination is generally perceived as equally severe across all gender groups. However, notable differences are visible in how players respond to harassment, with those from marginalized groups exhibiting higher levels of rumination. This highlights the compounded impact of gender-based toxicity in online gaming. These insights provide valuable direction for game developers seeking to create more inclusive and supportive gaming environments.

In today’s digitalized world, where software systems are becoming increasingly ubiquitous and complex, the quality aspect of explainability is gaining relevance. A major challenge in achieving adequate explanations is the elicitation of individual explanation needs, as it may be subject to severe hypothetical or confirmation biases. To address these challenges, we aim to establish user-based indicators concerning user behavior or system events that can be captured at runtime to determine when a need for explanations arises. In this work, we conducted exploratory research by means of an online study to collect self-reported indicators that could indicate a need for explanation. We compiled a catalog containing 17 relevant indicators concerning user behavior, 8 indicators concerning system events and 14 indicators concerning emotional states or physical reactions. We also analyze the relationships between these indicators and different types of need for explanation. The established indicators can be used in the elicitation process through prototypes, as well as after publication to gather requirements from already deployed applications using telemetry and usage data. Moreover, these indicators can be used to trigger explanations at appropriate moments during the runtime.

As software systems grow increasingly complex, explainability has become a crucial non-functional requirement for transparency, user trust, and regulatory compliance. Eliciting explainability requirements is challenging, as different methods capture varying levels of detail and structure. This study examines the efficiency and effectiveness of three commonly used elicitation methods—focus groups, interviews, and online surveys—while also assessing the role of taxonomy usage in structuring and improving the elicitation process. We conducted a case study at a large German IT consulting company, utilizing a web-based personnel management software. A total of two focus groups, 18 interviews, and an online survey with 188 participants were analyzed. The results show that interviews were the most efficient, capturing the highest number of distinct needs per participant per time spent. Surveys collected the most explanation needs overall but had high redundancy. Delayed taxonomy introduction resulted in a greater number and diversity of needs, suggesting that a two-phase approach is beneficial. Based on our findings, we recommend a hybrid approach combining surveys and interviews to balance efficiency and coverage. Future research should explore how automation can support elicitation and how taxonomies can be better integrated into different methods.

As the complexity of modern software is steadily growing, these systems become increasingly difficult to understand for their stakeholders. At the same time, opaque and artificially intelligent systems permeate a growing number of safety-critical areas, such as medicine and finance. As a result, explainability is becoming more important as a software quality aspect and non-functional requirement. Contemporary research has mainly focused on making artificial intelligence and its decision-making processes more understandable. However, explainability has also gained traction in recent requirements engineering research. This work aims to contribute to that body of research by providing a quality model for explainability as a software quality aspect. Quality models provide means and measures to specify and evaluate quality requirements. In order to design a user-centered quality model for explainability, we conducted a literature review. We identified ten fundamental aspects of explainability. Furthermore, we aggregated criteria and metrics to measure them as well as alternative means of evaluation in the form of heuristics and questionnaires. Our quality model and the related means of evaluation enable software engineers to develop and validate explainable systems in accordance with their explainability goals and intentions. This is achieved by offering a view from different angles at fundamental aspects of explainability and the related development goals. Thus, we provide a foundation that improves the management and verification of explainability requirements. • Literature review on criteria and measures for explainability. • Quality model for explainability including ten aspects of explainability. • User-centered metrics, heuristics and questionnaires to evaluate explainability.

Modern software systems are becoming increasingly complex and opaque. The integration of explanations within software has shown the potential to address this opacity and can make the system more understandable to end-users. As a result, explainability has gained much traction as a non-functional requirement of complex systems. Understanding what type of system requires what types of ex-planations is necessary to facilitate the inclusion of explainability in early software design processes. In order to specify explain-ability requirements, an explainability taxonomy that applies to a variety of different software types is needed. In this paper, we present the results of an online survey with 84 participants. We asked the participants to state their questions and confusions concerning their three most recently used software systems and elicited both explicit and implicit explainability needs from their statements. These needs were coded by three researchers. In total, we identified and classified 315 explainability needs from the survey answers. Drawing from a large pool of explainability needs and our coding procedure, we present two major contributions of this work: 1) a taxonomy for explainability needs in everyday software systems and 2) an overview of how the need for explanations differs between different types of software systems.

With the rise of artificial intelligence in industry, many companies rely on machine learning methods such as time series forecasting. By processing data from the past, such systems can provide predictions for data in the future. In practice, however, there is often skepticism about the quality of the forecasts. Explainability has been identified as a means to address this skepticism and foster trust. While there are already different methods to explain time series forecasts, it is unclear which of these explanations are actually useful for stakeholders. To investigate the need for explanations for time series forecasts, we conducted a study at a mid-sized German company in the energy domain. Throughout the study, 23 participants were shown five examples of different explanation types. For each type of explanation, we tested if it actually helped our participants to better understand the forecasts. We found that visual explanations including decision trees and feature importance charts were able to improve domain experts' understanding of time series forecasts. Textual explanations tended to lead to confusion rather than empowerment. While the exact findings and preferable types of explanations may vary between companies, our concrete results can provide a starting point for in-depth analyses in other environments.

Misalignments between stakeholders' project visions can lead to the elicitation of conflicting requirements. When these conflicts remain undetected, they can necessitate costly changes in late stages of the development process. One approach to avoid such conflicts are so-called vision videos. Vision videos present the project vision held by the stakeholders that create the vision videos. By watching a video created by their peers, stakeholders can detect and resolve misalignments between their project vision and the one presented in the video. Thus far, research has focused on how to create vision videos. Research on how to watch and use a vision video for requirements validation has been limited. If vision videos are created with care, but used without consideration, their full potential may be lost. In this paper, we aim to lay the groundwork for future research on the usage of vision videos by conducting an exploratory study with 128 students working in 21 project teams. Each team used vision videos to align their project vision with those of their customers. Based on this study, we identify 5 possible research avenues and present corresponding research questions. In doing so, we pave the way towards an effective usage of vision videos.

Explainability is an emerging quality aspect of software systems. Explanations offer a solution approach for achieving a variety of quality goals, such as transparency and user satisfaction. Therefore, explainability should be considered a means to an end. The evaluation of quality aspects is essential for successful software development. Evaluating explainability allows an assessment of the quality of explanations and enables the comparison of different explanation variants. As the evaluation depends on what quality goals the explanations are supposed to achieve, evaluating explainability is non-trivial. To address this problem, we combine the already well-established method of expert evaluation with goal-oriented heuristics. Goal-oriented heuristics are heuristics that are grouped with respect to the goals that the explanations are meant to achieve. By establishing appropriate goal-oriented heuristics, software engineers are enabled to evaluate explanations and identify problems with affordable resources. To show that this way of evaluating explainability is suitable, we conducted an interactive user study, using a high-fidelity software prototype. The results suggest that the alignment of heuristics with specific goals can enable an effective assessment of explainability.

In the context of the ongoing digitalization of society, human values such as privacy, ethics and trust are becoming increasingly important.Digital systems are entering private and professional spaces, which in turn affects the privacy of their end users.Hence, there is a need for conveying privacy information in a transparent and understandable manner, with the user in the focus.Lawmakers introduced privacy policies as a means of communicating privacy information.However, those documents have proven to be practically useless for end users.Privacy policies are long, vague, ambiguous and use complex language, such as legal terms, which often require profound background knowledge.Explainability has shown potential as a means to increase transparency and foster trust in software systems.Based upon the foundation of explainability, we developed a layered concept for usercentered privacy explanations, which is implemented within a high-fidelity software prototype.Finally, we tested and evaluated our concept by conducting an interactive user study with 61 participants.The results of our study suggest that our layered design concept enabled participants to understand the privacy aspects they regarded as important.We conclude that our approach seems to be an appropriate way to communicate complex privacy information to end users.