SUPPORTING THERAPY-CENTERED GAME DESIGN FOR BRAIN INJURY REHABILITATION

Brain injuries (BI) are a major public health issue. Many therapists who work with patients who have had a BI include games to ameliorate boredom associated with repetitive rehabilitation. However, designing effective, appropriate, and engaging games for BI therapy is challenging. The challenge is especially manifested when considering how to consolidate the different mindsets and motivations among key stakeholders; i.e., game designers and therapists. In this dissertation, I investigated the ideation, creation, and evaluation of game design patterns and a design tool, GaPBIT (Game Design Patterns for BI Therapy) that leveraged patterns to support ideation of BI therapy game concepts and facilitate communication among designers and therapists. Design patterns, originated from the work of Christopher Alexander, provide a common design language in a specific field by documenting reusable design concepts that have successfully solved recurring problems. This investigation involved four overlapping phases. In Phase One, I interviewed 11 professional game designers focused on games for health (serious games embedded with health-related goals) to explore how they perceived and approached their work. In Phase Two, I identified 25 therapy-centered game design patterns through analyzing data about game use in BI therapy. Based on those patterns, in Phase Three I created and iterated the GaPBIT prototype through user studies. In Phase Four, I conducted quasi-experimental case studies to establish the efficacy and user experience of GaPBIT in game design workshops that involved both game designers and therapists. During the design workshops, the design patterns and GaPBIT supported exploration of game design ideas and effectively facilitated discussion among designers and therapists. The results also indicated that these tools were especially beneficial for novice game designers. This work significantly promotes game design for BI rehabilitation by providing designers and therapists with easier access to the information about requirements in rehabilitation games. Additionally, this work modeled a novel research methodology for investigating domains where balancing the role of designers and other stakeholders is particularly important. Through a “practitioner-centered” process, this work also provides an exemplar of investigating technologies that directly address the information needs of professional practitioners

Characterizing Usability Issue Discussions in Open Source Software Projects

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.Comment: 26 pages, 2 figures, accepted to CSCW2024; supplementary material available at: https://github.com/HCDLab/UsabilityIssuesSupplementaryMateria

Adapting Nielsen's Usability Heuristics to the Context of Mobile Augmented Reality

Augmented reality (AR) is an emerging technology in mobile app design during recent years. However, usability challenges in these apps are prominent. There are currently no established guidelines for designing and evaluating interactions in AR as there are in traditional user interfaces. In this work, we aimed to examine the usability of current mobile AR applications and interpreting classic usability heuristics in the context of mobile AR. Particularly, we focused on AR home design apps because of their popularity and ability to incorporate important mobile AR interaction schemas. Our findings indicated that it is important for the designers to consider the unfamiliarity of AR technology to the vast users and to take technological limitations into consideration when designing mobile AR apps. Our work serves as a first step for establishing more general heuristics and guidelines for mobile AR.Comment: 3 pages, UIST '20 Poste

Analysis and Detection of Information Types of Open Source Software Issue Discussions

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.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019

Activity-Based Analysis of Open Source Software Contributors: Roles and Dynamics

Contributors to open source software (OSS) communities assume diverse roles to take different responsibilities. One major limitation of the current OSS tools and platforms is that they provide a uniform user interface regardless of the activities performed by the various types of contributors. This paper serves as a non-trivial first step towards resolving this challenge by demonstrating a methodology and establishing knowledge to understand how the contributors' roles and their dynamics, reflected in the activities contributors perform, are exhibited in OSS communities. Based on an analysis of user action data from 29 GitHub projects, we extracted six activities that distinguished four Active roles and five Supporting roles of OSS contributors, as well as patterns in role changes. Through the lens of the Activity Theory, these findings provided rich design guidelines for OSS tools to support diverse contributor roles.Comment: 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2019

Semantically Enhanced Software Traceability Using Deep Learning Techniques

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.Comment: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE

Capturing the Practices, Challenges, and Needs of Transportation Decision-Makers

Transportation decision-makers from government agencies play an important role in addressing the traffic network conditions, which in turn, have a major impact on the well-being of citizens. The practices, challenges, and needs of this group of practitioners are less represented in the HCI literature. We address this gap through an interview study with 19 practitioners from Transports Qu\'ebec, a government agency responsible for transportation infrastructures in Qu\'ebec, Canada. We found that this group of decision-makers can most benefit from research about data analysis tools and platforms that (1) provide information to support data quality awareness, (2) are interoperable with other tools in the complex workflow of the practitioners, and (3) support intuitive and customizable visual analytics. These implications can also be informative to the design of tools supporting other decision-making tasks and domains.Comment: 7 pages, 0 figures, ACM CHI LBW Paper (2020). For personas created in the project, see https://github.com/HCDLab/TDMPersona