Oliver Karras

Tim Wittenborg, Omar Imad Remmo, Claudia Frick et al.

Public broadcasts are at the center of civic discourse: Traditional television talk shows, alongside emerging podcast and web video formats, capture and guide the attention of our societies, shaping how citizens encounter politics, science, and societal issues. Yet, systematic or even simple analyses of these formats face similar challenges: guest and content metadata are scarce, fleeting, fragmented, and not standardized. Research conducted and questions answered are based on extensive, laborious, yet isolated data-curation efforts that capture only a fraction of the relevant landscape. This work seeks to address this issue using a scaling-oriented framework for FAIR data curation in public broadcasting. Evaluated on 15 broadcasting programs, the pipeline aggregates ZDF Archive PDFs, fernsehserien.de, and Wikidata into a unified knowledge graph. Of the 31,817 candidate guest mentions from these three sources, 17,729 could be automatically disambiguated, further 5,958 via 64 hours of manual reconciling using OpenRefine. Results are published at speakermining.wikibase.cloud and linked to Wikidata, enabling SPARQL-based question answering based on gender, age, occupation, or institutional affiliation across 8,436 canonical persons with 23,527 appearances in 6,469 aligned episodes. Our iterative experience reveals that correctly disambiguating and deduplicating speaker data from heterogeneous sources demands dedicated effort on sustainable infrastructure. For scalable and reliable question answering on public broadcasts to be accessible to everyone, we recommend fostering the potential of linked open data: Advancing alignment and utilization approaches like this work, particularly towards crowdsourced development and curation, but also more FAIR data interfaces from public broadcast service providers.

Lena John, Ahmed Malek Ghanmi, Tim Wittenborg et al.

As the volume of scientific literature grows, efficient knowledge organization becomes increasingly challenging. Traditional approaches to structuring scientific content are time-consuming and require significant domain expertise, highlighting the need for tool support. We present ExtracTable, a Human-in-the-Loop (HITL) workflow and framework that assists researchers in transforming unstructured publications into structured representations. The workflow combines large language models (LLMs) with user-defined schemas and is designed for downstream integration into knowledge graphs (KGs). Developed and evaluated in the context of the Open Research Knowledge Graph (ORKG), ExtracTable automates key steps such as document preprocessing and data extraction while ensuring user oversight through validation. In an evaluation with ORKG community participants following the Quality Improvement Paradigm (QIP), ExtracTable demonstrated high usability and practical value. Participants gave it an average System Usability Scale (SUS) score of 84.17 (A+, the highest rating). The time to progress from a research interest to literature-based insights was reduced from between 4 hours and 2 weeks to an average of 24:40 minutes. By streamlining corpus creation and structured data extraction for knowledge graph integration, ExtracTable leverages LLMs and user models to accelerate literature reviews. However, human validation remains essential to ensure quality, and future work will address improving extraction accuracy and entity linking to existing knowledge resources.

Sören Auer, Allard Oelen, Mohamad Yaser Jaradeh et al.

The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

Hamed Babaei Giglou, Jennifer D'Souza, Oliver Karras et al.

Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for ontology alignment, designed to address current limitations with existing tools faced by practitioners. Existing tools are limited in scalability, modularity, and ease of integration with recent AI advances. OntoAligner provides a flexible architecture integrating existing lightweight OA techniques such as fuzzy matching but goes beyond by supporting contemporary methods with retrieval-augmented generation and large language models for OA. The framework prioritizes extensibility, enabling researchers to integrate custom alignment algorithms and datasets. This paper details the design principles, architecture, and implementation of the OntoAligner, demonstrating its utility through benchmarks on standard OA tasks. Our evaluation highlights OntoAligner's ability to handle large-scale ontologies efficiently with few lines of code while delivering high alignment quality. By making OntoAligner open-source, we aim to provide a resource that fosters innovation and collaboration within the OA community, empowering researchers and practitioners with a toolkit for reproducible OA research and real-world applications.

Tim Wittenborg, Constantin Sebastian Tremel, Niklas Stehr et al.

Democratic societies need accessible, reliable information. Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI) curating non-textual media is still fragmented and not adequately equipped to scale against the content flood. Our work sets out to support the SciCom KI with a central, collaborative platform, the SciCom Wiki, to facilitate FAIR (findable, accessible, interoperable, reusable) media representation and the fact-checking of their content, particularly for videos and podcasts. Building an open-source service system centered around Wikibase, we survey requirements from 53 stakeholders, refine these in 11 interviews, and evaluate our prototype based on these requirements with another 14 participants. To address the most requested feature, fact-checking, we developed a neurosymbolic computational fact-checking approach, converting heterogenous media into knowledge graphs. This increases machine-readability and allows comparing statements against equally represented ground-truth. Our computational fact-checking tool was iteratively evaluated through 10 expert interviews, a public user survey with 43 participants verified the necessity and usability of our tool. Overall, our findings identified several needs to systematically support the SciCom KI. The SciCom Wiki, as a FAIR digital library complementing our neurosymbolic computational fact-checking framework, was found suitable to address the raised requirements. Further, we identified that the SciCom KI is severely underdeveloped regarding FAIR knowledge and related systems facilitating its collaborative creation and curation. Our system can provide a central knowledge node, yet a collaborative effort is required to scale against the imminent (mis-)information flood.

Lena John, Kheir Eddine Farfar, Sören Auer et al.

Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-star Linked Open Data (LOD) model, with five stages and defined criteria to guide the stepwise transition from a digital artifact, such as a PDF, to a semantic representation integrated in a knowledge graph (KG). Based on an exemplary workflow implementing the entire model, we developed a hybrid approach, called SciMantify, leveraging tabular formats of scientific knowledge, e.g., results from secondary studies, to support its evolving semantification. In the approach, humans and machines collaborate closely by performing semantic annotation tasks (SATs) and refining the results to progressively improve the semantic representation of scientific knowledge. We implemented the approach in the Open Research Knowledge Graph (ORKG), an established platform for improving the findability, accessibility, interoperability, and reusability of scientific knowledge. A preliminary user experiment showed that the approach simplifies the preprocessing of scientific knowledge, reduces the effort for the evolving semantification, and enhances the knowledge representation through better alignment with the KG structures.

Oliver Karras, Eduard C. Groen, Javed Ali Khan et al.

In recent decades, there has been a major shift towards improved digital access to scholarly works. However, even now that these works are available in digital form, they remain document-based, making it difficult to communicate the knowledge they contain. The next logical step is to extend these works with more flexible, fine-grained, semantic, and context-sensitive representations of scholarly knowledge. The Open Research Knowledge Graph (ORKG) is a platform that structures and interlinks scholarly knowledge, relying on crowdsourced contributions from researchers (as a crowd) to acquire, curate, publish, and process this knowledge. In this experience report, we consider the ORKG in the context of Crowd-based Requirements Engineering (CrowdRE) from two perspectives: (1) As CrowdRE researchers, we investigate how the ORKG practically applies CrowdRE techniques to involve scholars in its development to make it align better with their academic work. We determined that the ORKG readily provides social and financial incentives, feedback elicitation channels, and support for context and usage monitoring, but that there is improvement potential regarding automated user feedback analyses and a holistic CrowdRE approach. (2) As crowd members, we explore how the ORKG can be used to communicate scholarly knowledge about CrowdRE research. For this purpose, we curated qualitative and quantitative scholarly knowledge in the ORKG based on papers contained in two previously published systematic literature reviews (SLRs) on CrowdRE. This knowledge can be explored and compared interactively, and with more data than what the SLRs originally contained. Therefore, the ORKG improves access and communication of the scholarly knowledge about CrowdRE research. For both perspectives, we found the ORKG to be a useful multi-tool for CrowdRE research.

Lukas Nagel, Oliver Karras

One of the most important issues in requirements engineering (RE) is the alignment of stakeholders' mental models. Making sure that all stakeholders share the same vision of a changing system is crucial to the success of any project. Misaligned mental models of stakeholders can lead to conflicting requirements. A promising approach to this problem is the use of video showing a system vision, so-called vision videos, which help stakeholders to disclose, discuss, and align their mental models of the future system. However, videos have the drawback of allowing viewers to adopt a passive role, as has been shown in research on e-learning. In this role, viewers tend to be inactive, unfocused and bored while watching a video. In this paper, we learn and adopt findings from scientific literature in the field of e-learning on how to mitigate this passive role while watching vision videos in requirements engineering. In this way, we developed concepts that incorporate interactive elements into vision videos to help viewers stay focused. These elements include questions that are asked during the video and ways for viewers to decide what happens next in the video. In a preliminary evaluation with twelve participants, we found statistically significant differences when comparing the interactive vision videos with their traditional form. Using an interactive vision videos, viewers are noticeably more engaged and gather more information on the shown system.

Oliver Karras, Eklekta Kristo, Jil Klünder

Vision videos are established for soliciting feedback and stimulating discussions in requirements engineering (RE) practices, such as focus groups. Different researchers motivated the transfer of these benefits into crowd-based RE (CrowdRE) by using vision videos on social media platforms. So far, however, little research explored the potential of using vision videos for CrowdRE in detail. In this paper, we analyze and assess this potential, in particular, focusing on video comments as a source of feedback. In a case study, we analyzed 4505 comments on a vision video from YouTube. We found that the video solicited 2770 comments from 2660 viewers in four days. This is more than 50% of all comments the video received in four years. Even though only a certain fraction of these comments are relevant to RE, the relevant comments address typical intentions and topics of user feedback, such as feature request or problem report. Besides the typical user feedback categories, we found more than 300 comments that address the topic safety, which has not appeared in previous analyses of user feedback. In an automated analysis, we compared the performance of three machine learning algorithms on classifying the video comments. Despite certain differences, the algorithms classified the video comments well. Based on these findings, we conclude that the use of vision videos for CrowdRE has a large potential. Despite the preliminary nature of the case study, we are optimistic that vision videos can motivate stakeholders to actively participate in a crowd and solicit numerous of video comments as a valuable source of feedback.

Oliver Karras, Alexandra Risch, Jil Klünder

A wide variety of use case templates supports different variants to link a use case with its associated requirements. Regardless of the linking, a reader must process the related information simultaneously to understand them. Linking variants are intended to cause a specific reading behavior in which a reader interrelates a use case and its associated requirements. Due to the effort to create and maintain links, we investigated the impact of different linking variants on the reading behavior in terms of visual effort and the intended way of interrelating both artifacts. We designed an eye tracking study about reading a use case and requirements. We conducted the study twice each with 15 subjects as a baseline experiment and as a repetition. The results of the baseline experiment, its repetition, and their joint analysis are consistent. All investigated linking variants cause comparable visual effort. In all cases, reading the single artifacts one after the other is the most frequently occurring behavior. Only links embedded in the fields of a use case description significantly increase the readers' efforts to interrelate both artifacts. None of the investigated linking variants impedes reading a use case and requirements. However, only the most detailed linking variant causes readers to process related information simultaneously.

Jil Klünder, Melanie Busch, Natalie Dehn et al.

As software projects are very diverse, each software development process must be adjusted to the needs of the project and the corresponding development team. Frequently, we find different methods and practices combined in a so-called hybrid development method. Research has shown that these hybrid methods evolve over time and are devised based on experience. However, when devising a hybrid method, the methods and practices used should cover the whole software project with its different phases including, among others, project management, requirements analysis, quality management, risk management, and implementation. In this paper, we analyze which methods and practices are used in which phase of a software project. Based on an initial survey with 27 practitioners, we provide a mapping of methods and practices to different project phases and vice versa. Despite the preliminary nature of our study and the small sample size, we observe three remarkable aspects: (1) there are discrepancies between the intended use of methods and practices according to literature and the real use in practice, (2) practices are used more consistently than methods, and (3) parts of the software lifecycle such as maintenance and evolution are hardly covered by widely distributed methods and practices. Consequently, when devising a development process, it is worth a thought whether all phases of the software lifecycle are addressed or not.

Jil Klünder, Dzejlana Karajic, Paolo Tell et al.

Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts.

Oliver Karras, Svenja Polst, Kathleen Späth

Facilitated meetings are an established practice for the requirements engineering activities elicitation and validation. Focus groups are one well-known technique to implement this practice. Several researchers already reported the successful use of vision videos to stimulate active discussions among the participants of on-site focus groups, e.g., for validating scenarios and eliciting feedback. These vision videos show scenarios of a system vision. In this way, the videos serve all parties involved as a visual reference point to actively disclose, discuss, and align their mental models of the future system to achieve shared understanding. In the joint project TrUSD, we had planned to conduct such an on-site focus group using a vision video to validate a scenario of a future software tool, the so-called Privacy Dashboard. However, the COVID-19 pandemic and its associated measures led to an increase in home and remote working, which also affected us. Therefore, we had to replan and conduct the focus group virtually. In this paper, we report about our experiences and recommendations for the use of vision videos in virtual focus groups.

Jil Klünder, Julian Horstmann, Oliver Karras

Software development encompasses many collaborative tasks in which usually several persons are involved. Close collaboration and the synchronization of different members of the development team require effective communication. One established communication channel are meetings which are, however, often not as effective as expected. Several approaches already focused on the analysis of meetings to determine the reasons for inefficiency and dissatisfying meeting outcomes. In addition to meetings, text-based communication channels such as chats and e-mails are frequently used in development teams. Communication via these channels requires a similar appropriate behavior as in meetings to achieve a satisfying and expedient collaboration. However, these channels have not yet been extensively examined in research. In this paper, we present an approach for analyzing interpersonal behavior in text-based communication concerning the conversational tone, the familiarity of sender and receiver, the sender's emotionality, and the appropriateness of the used language. We evaluate our approach in an industrial case study based on 1947 messages sent in a group chat in Zulip over 5.5 months. Using our approach, it was possible to automatically classify written sentences as positive, neutral, or negative with an average accuracy of 62.97% compared to human ratings. Despite this coarse-grained classification, it is possible to gain an overall picture of the adequacy of the textual communication and tendencies in the group mood.

Oliver Karras, Kurt Schneider

Background and Motivation: In recent years, the topic of applying videos in requirements engineering has been discussed and its contributions are of interesting potential. In the last 35 years, several researchers proposed approaches for applying videos in requirements engineering due to their communication richness and effectiveness. However, these approaches mainly use videos but omit the details about how to produce them. This lack of guidance is one crucial reason why videos are not an established documentation option for successful requirements communication and thus shared understanding. Software professionals are not directors and thus they do not necessarily know what constitutes a good video in general and for an existing approach. Therefore, this lack of knowledge and skills on how to produce and use videos for visual communication impedes the application of videos by software professionals in requirements engineering. How to Create Effective Videos and Vision Videos?: This technical report addresses this lack of knowledge and skills by software professionals. We provide two guidelines that can be used as checklists to avoid frequent flaws in the production and use of videos respectively vision videos. Software professionals without special training should be able to follow these guidelines to achieve the basic capabilities to produce (vision) videos that are accepted by their stakeholders. These guidelines represent a core set of those capabilities in the preproduction, shooting, postproduction, and viewing of (vision) videos. We do not strive for perfection in any of these capabilities, .e.g., technical handling of video equipment, storytelling, or video editing. Instead, these guidelines support all steps of the (vision) video production and use process to a balanced way.

Oliver Karras, Kurt Schneider, Samuel A. Fricker

Establishing a shared software project vision is a key challenge in Requirements Engineering (RE). Several approaches use videos to represent visions. However, these approaches omit how to produce a good video. This missing guidance is one crucial reason why videos are not established in RE. We propose a quality model for videos representing a vision, so-called vision videos. Based on two literature reviews, we elaborate ten quality characteristics of videos and five quality characteristics of visions which together form a quality model for vision videos that includes all 15 quality characteristics. We provide two representations of the quality model: (a) A hierarchical decomposition of vision video quality into the quality characteristics and (b) A mapping of these characteristics to the video production and use process. While the hierarchical decomposition supports the evaluation of vision videos, the mapping provides guidance for video production. In an evaluation with 139 students, we investigated whether the 15 characteristics are related to the overall quality of vision videos perceived by the subjects from a developer's the point of view. Six characteristics (video length, focus, prior knowledge, clarity, pleasure, and stability) correlated significantly with the likelihood that the subjects perceived a vision video as good. These relationships substantiate a fundamental relevance of the proposed quality model. Therefore, we conclude that the quality model is a sound basis for future refinements and extensions.

Oliver Karras, Jil Klünder, Kurt Schneider

The adequate use of documentation for communication is one challenge in requirements engineering (RE). In recent years, several researchers addressed this challenge by using videos as a communication mechanism. All of them concluded that this way of using videos has the potential to facilitate requirements communication. Nevertheless, software professionals are not directors and thus do not necessarily know what constitutes a good video. This lack of knowledge is one crucial reason why videos are still not an established communication mechanism in RE. When videos shall be established in the RE activities, practices, and techniques, requirements engineers have to acquire the necessary knowledge to produce and use good videos on their own at moderate costs, yet sufficient quality. In our research project ViViReq (see Acknowledgment), we aspire to bridge this knowledge gap about what constitutes a good video. Whether a video is good or not depends on its quality perceived by its viewers. However, video quality is a rather ill-defined concept due to numerous unspecified technical and subjective characteristics. As part of our research plan, we develop a quality model for videos inspired by the idea of Femmer and Vogelsang to define and evaluate the quality of videos as RE artifacts. In addition to evaluating videos, this quality model can be used to identify the relevant characteristics of videos for their specific purpose which can be further used to specify requirements, their criteria for satisfaction, and corresponding measures. Therefore, software professionals may use the quality model as guidance for producing and using videos.

Kurt Schneider, Melanie Busch, Oliver Karras et al.

[Context and motivation] Complex software-based systems involve several stakeholders, their activities and interactions with the system. Vision videos are used during the early phases of a project to complement textual representations. They visualize previously abstract visions of the product and its use. By creating, elaborating, and discussing vision videos, stakeholders and developers gain an improved shared understanding of how those abstract visions could translate into concrete scenarios and requirements to which individuals can relate. [Question/problem] In this paper, we investigate two aspects of refining vision videos: (1) Refining the vision by providing alternative answers to previously open issues about the system to be built. (2) A refined understanding of the camera perspective in vision videos. The impact of using a subjective (or "ego") perspective is compared to the usual third-person perspective. [Methodology] We use shopping in rural areas as a real-world application domain for refining vision videos. Both aspects of refining vision videos were investigated in an experiment with 20 participants. [Contribution] Subjects made a significant number of additional contributions when they had received not only video or text but also both - even with very short text and short video clips. Subjective video elements were rated as positive. However, there was no significant preference for either subjective or non-subjective videos in general.

Oliver Karras

In requirements engineering (RE), knowledge is mainly communicated via written specifications. This practice is cumbersome due to its low communication richness and effectiveness. In contrast, videos can transfer knowledge more richly and effectively. However, video is still a neglected medium in RE. We investigate if software professionals perceive video as a medium that can contribute to RE. We focus on their attitudes towards video as a medium in RE including its strengths, weaknesses, opportunities, and threats. We conducted a survey to explore these attitudes with a questionnaire. 64 out of 106 software professionals completed the survey. The respondents' overall attitude towards video is positive. 59 of them stated that video has the potential to improve RE. However, 34 respondents also mentioned threats of videos for RE. We identified the strengths, weaknesses, opportunities, and threats of videos for RE from the point of view of software professionals. Video is a medium with a neglected potential. Software professionals do not fundamentally reject videos in RE. Despite the strengths and opportunities of video, the stated weaknesses and threats impede its application. Based on our findings, we conclude that software professionals need guidance on how to produce and use videos for visual communication to take full advantage of the currently neglected potential.

Oliver Karras, Kurt Schneider

Videos are one of the best documentation options for a rich and effective communication. They allow experiencing the overall context of a situation by representing concrete realizations of certain requirements. Despite 35 years of research on integrating videos in requirements engineering (RE), videos are not an established documentation option in terms of RE best practices. Several approaches use videos but omit the details about how to produce them. Software professionals lack knowledge on how to communicate visually with videos since they are not directors. Therefore, they do not necessarily have the required skills neither to produce good videos in general nor to deduce what constitutes a good video for an existing approach. The discipline of video production provides numerous generic guidelines that represent best practices on how to produce a good video with specific characteristics. We propose to analyze this existing know-how to learn what constitutes a good video for visual communication. As a plan of action, we suggest a literature study of video production guidelines. We expect to identify quality characteristics of good videos in order to derive a quality model. Software professionals may use such a quality model for videos as an orientation for planning, shooting, post-processing, and viewing a video. Thus, we want to encourage and enable software professionals to produce good videos at moderate costs, yet sufficient quality.

Zahra Shakeri Hossein Abad, Oliver Karras, Kurt Schneider et al.

Multitasking has always been an inherent part of software development and is known as the primary source of interruptions due to task switching in software development teams. Developing software involves a mix of analytical and creative work, and requires a significant load on brain functions, such as working memory and decision making. Thus, task switching in the context of software development imposes a cognitive load that causes software developers to lose focus and concentration while working thereby taking a toll on productivity. To investigate the disruptiveness of task switching and interruptions in software development projects, and to understand the reasons for and perceptions of the disruptiveness of task switching we used a mixed-methods approach including a longitudinal data analysis on 4,910 recorded tasks of 17 professional software developers, and a survey of 132 software developers. We found that, compared to task-specific factors (e.g. priority, level, and temporal stage), contextual factors such as interruption type (e.g. self/external), time of day, and task type and context are a more potent determinant of task switching disruptiveness in software development tasks. Furthermore, while most survey respondents believe external interruptions are more disruptive than self-interruptions, the results of our retrospective analysis reveals otherwise. We found that self-interruptions (i.e. voluntary task switchings) are more disruptive than external interruptions and have a negative effect on the performance of the interrupted tasks. Finally, we use the results of both studies to provide a set of comparative vulnerability and interaction patterns which can be used as a mean to guide decision-making and forecasting the consequences of task switching in software development teams.

Kurt Schneider, Oliver Karras, Anne Finger et al.

Challenges in spatial planning include adjusting settlement patterns to increasing or shrinking populations; it also includes organizing food delivery in rural and peripheral environments. Discourse typically starts with an open problem and the search for a holistic and innovative solution. Software will often be needed to implement the innovation. Spatial planning problems are characterized by large and heterogeneous groups of stakeholders, such as municipalities, companies, interest groups, citizens, women and men, young people and children. Current techniques for participation are slow, laborious and costly, and they tend to miss out on many stakeholders or interest groups. We propose a triple shift in perspective: (1) Discourse is reframed as a requirements process with the explicit goal to state software, hardware, and organizational requirements. (2) Due to the above-mentioned characteristics of spatial planning problems, we suggest using techniques of requirements engineering (RE) and CrowdRE for getting stakeholders (e.g. user groups) involved. (3) We propose video as a medium for communicating problems, solution alternatives, and arguments effectively within a mixed crowd of officials, citizens, children and elderly people. Although few spatial planning problems can be solved by software alone, this new perspective helps to focus discussions anyway. RE techniques can assist in finding common ground despite the heterogeneous group of stakeholders, e.g. citizens. Digital requirements and video are well-suited for facilitating distribution, feedback, and discourse via the internet. In this paper, we propose this new perspective as a timely opportunity for the spatial planning domain - and as an increasingly important application domain of CrowdRE.

Oliver Karras, Carolin Unger-Windeler, Lennart Glauer et al.

Requirements engineering provides several practices to analyze how a user wants to interact with a future software. Mockups, prototypes, and scenarios are suitable to understand usability issues and user requirements early. Nevertheless, users are often dissatisfied with the usability of a resulting software. Apparently, previously explored information was lost or no longer accessible during the development phase. Scenarios are one effective practice to describe behavior. However, they are commonly notated in natural language which is often improper to capture and communicate interaction knowledge comprehensible to developers and users. The dynamic aspect of interaction is lost if only static descriptions are used. Digital prototyping enables the creation of interactive prototypes by adding responsive controls to hand- or digitally drawn mockups. We propose to capture the events of these controls to obtain a representation of the interaction. From this data, we generate videos, which demonstrate interaction sequences, as additional support for textual scenarios. Variants of scenarios can be created by modifying the captured event sequences and mockups. Any change is unproblematic since videos only need to be regenerated. Thus, we achieve video as a by-product of digital prototyping. This reduces the effort compared to video recording such as screencasts. A first evaluation showed that such a generated video supports a faster understanding of a textual scenario compared to static mockups.

Oliver Karras, Jil Klünder, Kurt Schneider

The task board is an essential artifact in many agile development approaches. It provides a good overview of the project status. Teams often customize their task boards according to the team members' needs. They modify the structure of boards, define colored codings for different purposes, and introduce different card sizes. Although the customizations are intended to improve the task board's usability and effectiveness, they may also complicate its comprehension and use. The increased effort impedes the work of both the team and team externals. Hence, task board customization is in conflict with the agile practice of fast and easy overview for everyone. In an eye tracking study with 30 participants, we compared an original task board design with three customized ones to investigate which design shortened the required time to identify a particular story card. Our findings yield that only the customized task board design with modified structures reduces the required time. The original task board design is more beneficial than individual colored codings and changed card sizes. According to our findings, agile teams should rethink their current task board design. They may be better served by focusing on the original task board design and by applying only carefully selected adjustments. In case of customization, a task board's structure should be adjusted since this is the only beneficial kind of customization, that additionally complies more precisely with the concept of fast and easy project overview.

Zahra Shakeri Hossein Abad, Oliver Karras, Parisa Ghazi et al.

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.