Open-source software (OSS) developers often struggle to understand and respond to user context, while existing tools, such as issue trackers (for handling bugs, requests, and feedback), largely focus on technical discussion. Although personas could help, limited resources and UX expertise make them hard to scale. We present PersonaFlow, a tool that generates editable user personas from OSS repository artifacts and integrates them alongside issue reports. In a user study with 13 OSS developers, most reported shifts in how they understood users, and more than half modified their responses by adding empathetic language, tailoring explanations, or raising priority ratings. We found two pathways to this change: some connected emotionally to personas as people, while others used them pragmatically for triaging. Both appeared to lead to more user-centered behavior. We contribute design implications for persona-based tools relevant to OSS and other contexts where efficiency-driven systems or workflows obscure valuable human elements.

Divergent thinking activities, like research and ideation, are key drivers of innovation in UI/UX design. Existing research has explored AI's role in automating design tasks, but leaves a critical gap in understanding how AI specifically influences divergent thinking. To address this, we conducted interviews with 19 professional UI/UX designers, examining their use and perception of AI in these creative activities. We found that in this context, participants valued AI tools that offer greater control over ideation, facilitate collaboration, enhance efficiency to liberate creativity, and align with their visual habits. Our results indicated four key roles AI plays in supporting divergent thinking: aiding research, kick-starting creativity, generating design alternatives, and facilitating prototype exploration. Through this study, we provide insights into the evolving role of AI in the less-investigated area of divergent thinking in UI/UX design, offering recommendations for future AI tools that better support design innovation.

Figure 1: UIDEC allows UI/UX designers to specify design constraints 1 ○, generate and organize design ideas on a canvas 2 ○, refine or regenerate specific UI elements 3 ○, navigate through different generated versions 4 ○, and save and arrange design examples 5○, among other features.We investigated the design of UIDEC to understand designers' needs in creative ideation under constraints and to identify opportunities for using AI-powered tools to support this process.

Addressing usability in free, libre, and open-source software (FLOSS) is a challenging issue, particularly due to a long-existing ''by developer, for developer'' mentality. Engaging designers and end-users to work with developers can help improve its usability, but unequal power dynamics among those stakeholder roles must be mitigated. To explore how the power of different FLOSS stakeholders manifests and can be mediated during collaboration, we conducted eight design workshops with different combinations of key FLOSS stakeholders (i.e., developers, designers, and end-users). Leveraging existing theories on Dimensions of Power, we revealed how participants navigate existing role-based power structures through resource utilization, knowledge gap management, and experience referencing. We also observed that participants exhibited diverse behaviors confirming and challenging the status quo of FLOSS usability. Overall, our results contribute to a comprehensive understanding of the power dynamics among FLOSS stakeholders, providing valuable insights into ways to balance their power to improve FLOSS usability. Our work also serves as an exemplar of using design workshops as a research method to study power dynamics during collaboration that are usually hidden in the field.

Game modding offers unique and personalized gaming experiences, but the technical complexity of creating mods often limits participation to skilled users. We envision a future where every player can create personalized mods for their games. To explore this space, we designed StarCharM, a GenAI-based non-player character (NPC) creator for Stardew Valley. Our tool enables players to iteratively create new NPC mods, requiring minimal user input while allowing for fine-grained adjustments through user control. We conducted a user study with ten Stardew Valley players who had varied mod usage experiences to understand the impacts of StarCharM and provide insights into how GenAI tools may reshape modding, particularly in NPC creation. Participants expressed excitement in bringing their character ideas to life, although they noted challenges in generating rich content to fulfill complex visions. While they believed GenAI tools like StarCharM can foster a more diverse modding community, some voiced concerns about diminished originality and community engagement that may come with such technology. Our findings provided implications and guidelines for the future of GenAI-powered modding tools and co-creative modding practices.

In an era of AI's growing capabilities and influences, recent advancements are reshaping HCI and CSCW's view of AI. Playful interactions emerged as an important way for users to make sense of the ever-changing AI technologies, yet remained underexamined. We target this gap by investigating playful interactions exhibited by users of a popular AI technology, ChatGPT. Through a thematic analysis of 372 user-generated posts on the ChatGPT subreddit, we found that more than half (54%) of user discourse revolved around playful interactions. The analysis further allowed us to construct a preliminary framework to describe these interactions, categorizing them into six types: reflecting, jesting, imitating, challenging, tricking, and contriving; each included sub-categories. This study contributes to HCI and CSCW by identifying the diverse ways users engage in playful interactions with AI. It examines how these interactions can help users understand AI's agency, shape human-AI relationships, and provide insights for designing AI systems.

Software development relies on effective collaboration between Software Development Engineers (SDEs) and User eXperience Designers (UXDs) to create software products of high quality and usability. While this collaboration issue has been explored over the past decades, anecdotal evidence continues to indicate the existence of challenges in their collaborative efforts. To understand this gap, we first conducted a systematic literature review (SLR) of 45 papers published since 2004, uncovering three key collaboration challenges and two main categories of potential best practices. We then analyzed designer and developer forums and discussions from one open-source software repository to assess how the challenges and practices manifest in the status quo. Our findings have broad applicability for collaboration in software development, extending beyond the partnership between SDEs and UXDs. The suggested best practices and interventions also act as a reference for future research, assisting in the development of dedicated collaboration tools for SDEs and UXDs.

In modern technology environments, raising users’ privacy awareness is crucial. Existing efforts largely focused on privacy policy presentation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design effectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that effective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication.

Poorly managed postoperative acute pain can have long-lasting negative impacts and pose a major healthcare issue. There is limited investigation to understand and address the unique needs of patients experiencing acute pain. In this paper, we tackle this gap through an interview study with 14 patients who recently underwent postoperative acute pain to understand their challenges in pain self-management and their need for supportive tools. Our analysis identified various factors associated with the major aspects of acute pain self-management. Together, our findings indicated that tools for supporting these patients need to carefully consider information and support delivery to adapt to rapid changes in pain experiences, offer personalized and dynamic assistance that adapts to individual situations in context, and monitor emotion when promoting motivation. Overall, our work provided valuable knowledge to address the less-investigated but highly-needed problem of designing technology for the self-management of acute pain and similar health conditions.

Usability is a crucial factor but one of the most neglected concerns in open source software (OSS). While far from an ideal approach, a common practice that OSS communities adopt to collaboratively address usability is through discussions on issue tracking systems (ITSs). However, there is little knowledge about the extent to which OSS community members engage in usability issue discussions, the aspects of usability they frequently target, and the characteristics of their collaboration around usability issue discussions. This knowledge is important for providing practical recommendations and research directions to better support OSS communities in addressing this important topic and improve OSS usability in general. To help achieve this goal, we performed an extensive empirical study on issues discussed in five popular OSS applications: three data science notebook projects (Jupyter Lab, Google Colab, and CoCalc) and two code editor projects (VSCode and Atom). Our results indicated that while usability issues are extensively discussed in the OSS projects, their scope tended to be limited to efficiency and aesthetics. Additionally, these issues are more frequently posted by experienced community members and display distinguishable characteristics, such as involving more visual communication and more participants. Our results provide important implications that can inform the OSS practitioners to better engage the community in usability issue discussion and shed light on future research efforts toward collaboration techniques and tools for discussing niche topics in diverse communities, such as the usability issues in the OSS context.

Automated recruitment tools are proliferating. While having the promise of improving efficiency, various risks, including bias, challenges the potential of these tools. An in-depth understanding of the perceived risk factors and needs from the perspective of both recruiters and job seekers is needed. We address this through an interview study in the high-tech industry to compare and contrast the concerns of these two roles. We found that the importance of clarifying position requirements and assessing candidates as “whole individuals” are commonly discussed by both recruiters and job seekers. In contrast, while recruiters tended to be more aware of cognitive bias and desired more tool support during interviews, job seekers voiced more desire towards a healthy candidate-company relationship. Additionally, both roles considered the uncertainty of the current technology capability and reduced human contact as concerns for using automated tools. Based on these results, we provided design implications for automated recruitment tools and related decision-support technologies.

Issue Tracking Systems (ITS) often support commenting on software issues, which creates a space for discussions centered around bug fixes and improvements to the software. For Open Source Software (OSS) projects, issue discussions serve as a crucial collaboration mechanism for diverse stakeholders. However, these discussions can become lengthy and entangled, making it hard to find relevant information and make further contributions. In this work, we study the use of summarization to aid users in collaboratively making sense of OSS issue discussion threads. Through an empirical investigation, we reveal a complex picture of how summarization is used by issue users in practice as a strategy to help develop and manage their discussions. Grounded on the different objectives served by the summaries and the outcome of our formative study with OSS stakeholders, we identified a set of guidelines to inform the design of collaborative summarization tools for OSS issue discussions. We then developed SUMMIT, a tool that allows issue users to collectively construct summaries of different types of information discussed, as well as a set of comments representing continuous conversations within the thread. To alleviate the manual effort involved, SUMMIT uses techniques that automatically detect information types and summarize texts to facilitate the generation of these summaries. A lab user study indicates that, as the users of SUMMIT, OSS stakeholders adopted different strategies to acquire information on issue threads. Furthermore, different features of SUMMIT effectively lowered the perceived difficulty of locating information from issue threads and enabled the users to prioritize their effort. Overall, our findings demonstrated the potential of SUMMIT, and the corresponding design guidelines, in supporting users to acquire information from lengthy discussions in ITSs. Our work sheds light on key design considerations and features when exploring crowd-based and machine-learning-enabled instruments for asynchronous collaboration on complex tasks such as OSS development.

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.

Most issue tracking systems for open source software (OSS) development include features for community members to embed visual contents, such as images and videos, to enhance the discussion. Although playing an important role, there is little knowledge on the characteristics of the visual contents to support their use. To address this gap, we conducted an empirical study on the Jupyter Notebook project. We found that more than a quarter of the issues in the Jupyter Notebook project included visual contents. Additionally, issues that had visual contents in the comments but not in the issue posts tended to have a longer discussion and to involve a larger number of discussants. In our qualitative analysis, we identified eight types of visual contents in our sample, with about 60% including screenshots or mockups of the main product. We also found that visual contents served diverse purposes, touching both problem and solution spaces of the issues. Our effort serves as an important step towards a comprehensive understanding of the characteristics of visual contents in OSS issue discussions. Our results provided several design implications for issue tracking systems to better facilitate the use of visual contents.

Although issues of open source software are created to discuss and solve technical problems, conversations can become heated, with discussants getting angry and/or agitated for a variety of reasons, such as poor suggestions or violation of community conventions. To prevent and mitigate discussions from getting heated, tools like GitHub have introduced the ability to lock issue discussions that violate the code of conduct or other community guidelines. Despite some early research on locked issues, there is a lack of understanding of how communities use this feature and of potential threats to validity for researchers relying on a dataset of locked issues as an oracle for heated discussions. To address this gap, we (i) quantitatively analyzed 79 GitHub projects that have at least one issue locked as too heated, and (ii) qualitatively analyzed all issues locked as too heated of the 79 projects, a total of 205 issues comprising 5,511 comments. We found that projects have different behaviors when locking issues: while 54 locked less than 10% of their closed issues, 14 projects locked more than 90% of their closed issues. Additionally, locked issues tend to have a similar number of comments, participants, and emoji reactions to non-locked issues. For the 205 issues locked as too heated, we found that one-third do not contain any uncivil discourse, and only 8.82% of the analyzed comments are actually uncivil. Finally, we found that the locking justifications provided by maintainers do not always match the label used to lock the issue. Based on our results, we identified three pitfalls to avoid when using the GitHub locked issues data and we provide recommendations for researchers and practitioners.

Code review is an important quality assurance activity for software development. Code review discussions among developers and maintainers can be heated and sometimes involve personal attacks and unnecessary disrespectful comments, demonstrating, therefore, incivility. Although incivility in public discussions has received increasing attention from researchers in different domains, the knowledge about the characteristics, causes, and consequences of uncivil communication is still very limited in the context of software development, and more specifically, code review. To address this gap in the literature, we leverage the mature social construct of incivility as a lens to understand confrontational conflicts in open source code review discussions. For that, we conducted a qualitative analysis on 1,545 emails from the Linux Kernel Mailing List (LKML) that were associated with rejected changes. We found that more than half (66.66%) of the non-technical emails included uncivil features. Particularly, frustration, name calling, and impatience are the most frequent features in uncivil emails. We also found that there are civil alternatives to address arguments, while uncivil comments can potentially be made by any people when discussing any topic. Finally, we identified various causes and consequences of such uncivil communication. Our work serves as the first study about the phenomenon of in(civility) in open source software development, paving the road for a new field of research about collaboration and communication in the context of software engineering activities.

The growing adoption of unmanned aerial vehicles (UAVs) for tasks such as eCommerce, aerial surveillance, and environmental monitoring introduces the need for new safety mechanisms in an increasingly cluttered airspace. In our work we thus emphasize safety issues that emerge at the intersection of infrastructures responsible for controlling the airspace, and the diverse UAVs operating in their space. We build on safety assurance cases (SAC) - a state-of-the-art solution for reasoning about safety - and propose a novel approach based on interlocking SACs. The infrastructure safety case (ISAC) specifies assumptions upon UAV behavior, while each UAV demonstrates compliance to the ISAC by presenting its own (pluggable) safety case (pSAC) which connects to the ISAC through a set of interlock points. To collect information on each UAV we enforce a “trust but monitor” policy, supported by runtime monitoring and an underlying reputation model. We evaluate our approach in three ways: first by developing ISACs for two UAV infrastructures, second by running simulations to evaluate end-to-end effectiveness, and finally via an outdoor field-study with physical UAVs. The results show that interlocking SACs can be effective for identifying, specifying, and monitoring safety-related constraints upon UAVs flying in a controlled airspace.

In open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as issue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of community opinions in ITSs. Through content analysis, we anatomized highly discussed usability issues from a large, active OSS project, into their argumentation components and standpoints. We then experimented with supervised machine learning techniques for automated argument extraction. Finally, through a study with experienced ITS users, we show that the information provided by ArguLens supported the digestion of usability-related opinions and facilitated the review of lengthy issues. ArguLens provides the direction of designing valuable tools for high-level reasoning and effective discussion about usability.

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.

In most safety-critical domains the need for traceability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts, however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links, however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.

Developers maintaining safety-critical systems need to assess the impact a proposed change would have upon existing safety controls. By leveraging the network of traceability links that are present in most safety-critical systems, we can push timely information about related hazards, environmental assumptions, and safety requirements to developers. In this work we take a design science approach to discover the informational needs of developers as they engage in software maintenance activities and then propose and evaluate techniques for presenting and visualizing this information. Through a human-centered study involving five safety-critical system practitioners and 14 experienced developers, we analyze the way in which developers use requirements knowledge while maintaining safety-critical code, identify their informational needs, and propose and evaluate a supporting visualization technique. The insights proposed as a result of this study can be used to design requirements-based knowledge tools for supporting developers' maintenance tasks.

Brain injury (BI) is recognized as a major health issue. It is common for therapists to include commercial-off-the-shelf (COTS) games in their therapies to help motivate patients who have had a BI engage in rehabilitation tasks. In this paper, we present a prototype 'Choose a Game' tool that focuses on helping therapists select appropriate games that match their therapeutic goals and patient attributes. The tool leveraged a knowledge-base that we created about COTS games use in BI therapy. We evaluated the prototype through user studies with 29 therapists at two rehabilitation hospitals. While further improvements are needed, the tool enabled therapists to use games from a wider range of selections and therapists were generally satisfied with the game recommendations and the tool's user experience. This project is also a demonstration of a novel research model for investigating domains where technologies are rapidly proliferating for users with wide-ranging attributes such as the domain of therapeutic gaming for BI rehabilitation.

Games are often used in brain injury (BI) therapy sessions to help motivate patients to engage in rehabilitation activities. In this paper, we explore game design patterns as a mechanism to help game designers understand needs in BI therapy. Design patterns, originating from the work of Christopher Alexander, aim to provide a common language to support the creative work of designers by documenting solutions that have successful addressed recurring design problems. Through analyzing data we gathered on the use of commercial games in BI therapy, we generated a list of 14 'efficacy-centered game design patterns' that focused on game design considerations when addressing therapeutic goals in BI rehabilitation. We argue that our patterns can serve as a common language to support the design of BI rehabilitation games; additionally, our data-driven approach sets up a paradigm for generating game design patterns in related areas.

As information and communication technologies (ICTs) become more diffuse, the diversity of users that designers need to consider is growing; this includes people with disabilities and aging populations. As a result, computing education must provide students the means and inspiration to learn about inclusive design. This poster presents top-level findings from 18 interviews with professors from some of the top universities in the US. Our analysis yielded four categories of findings: (1) important student learning outcomes (the most common was for students to embrace diversity); (2) exercises and teaching materials (almost all focused on inclusion of people with disabilities in discovery and evaluation of ICTs); (3) frustrations and challenges (largely focused on how to engage students in accessibility topics); and (4) the importance of instructor initiative to include the topic of accessibility in their teaching. The unifying theme was the high importance of cultivating empathy with end users.

Brain injuries (BI) are recognized as a major public health issue. Many therapists include commercial motion-based videogames in their therapy sessions to help make rehabilitation exercises fun and engaging. Our initial exploratory work exposed a need for tools to help therapists make evidence-based decisions when choosing commercial motion-games for their patients who have had a BI. Targeting this need, we are gathering requirements for a case-based recommender (CBR) system that will act as a decision tool for therapists. In this paper, we describe our ongoing work as a case study that illustrates our multi-method approach of requirement elicitation for the CBR system. Our approach is comprised of four overlapping steps: (1) interviews with therapists, (2) onsite observations of therapy game sessions, (3) diary studies in which therapists record detailed information about game sessions, and (4) a user study of a CBR prototype interface. Leveraging direct interaction with end users (i.e., therapists), this case study demonstrates requirements gathering techniques to address needs of a special population (i.e., therapists who work with patients who had BIs) in a specialized context (i.e., inpatient rehabilitation using motion-based video games).

In this project, we explored how commercial motion-based video games were used in a rehabilitation hospital with patients who have had a brain injury (BI). We interviewed therapists and observed game sessions. Major findings included: (a) the social aspects of gaming were highly valued; (b) therapists had varied physical, cognitive and social goals when using games; and (c) therapists made game decisions primarily based on familiarity versus choosing games that best match therapeutic goals and patient profiles. Our exploration exposed a need for decision tools to help therapists make evidence-based decisions about commercial games; i.e. to help them choose games that match session goals and patient profiles. We have expanded our research to include diary studies in order to gather data for 'seed cases' for decision tools that use case-based reasoning.

In this paper, we present survey findings about how user experience (UX) and human-computer interaction (HCI) professionals, who create information and communication technologies (ICTs), reported considering accessibility in their work. Participants (N = 199) represented a wide range of job titles and nationalities. We found that most respondents (87%, N = 173) reported that accessibility was important or very important in their work; however, when considerations for accessibility were discussed in an open-ended question (N =185) the scope was limited. Additionally, we found that aspects of empathy and professional experience were associated with how accessibility considerations were reported. We also found that many respondents indicated that decisions about accessibility were not in their control. We argue that a better understanding about how accessibility is considered by professionals has implications for academic programs in HCI and UX as to how well programs are preparing students to consider and advocate for inclusive design.