Abstract

Openness to Experience is a core dimension of the Five Factor Model (FFM). It is associated with aesthetic sensitivity and cognitive exploration. Traditional assessments rely on self-report and informant-report, which are susceptible to biases such as social desirability and inaccurate self-perception. Behavioral measures offer a complementary approach, but existing methods often lack controlled yet semi-naturalistic valid settings.

In this work, we design an affectively controlled virtual reality (VR) art gallery to study and infer Openness to Experience through semi-naturalistic interaction. The experiment was conducted in two phases. To ensure cultural validity, firstly, we re-evaluate a subset of artworks from the VAPS dataset on an Indian population (N=30) and construct a stimulus set of paintings and sculptures with controlled valence, arousal, and visual complexity. Secondly, in the VR art gallery, we capture five behavioral signals from participants (N=49), spanning locomotor signals (gallery dwell time, total distance walked, average walking speed), oculomotor signals (total fixation duration), and physical interaction signals (sculpture rotations).

Results show that Indian participants rate artworks as more visually complex compared to prior VAPS ratings. We further find that total gallery dwell time is strongly associated with Openness to Experience ($rho = 0.64$, $p < 0.001$), alongside other behavioral signals. Building on these findings, we evaluate a predictive framework using five classifiers, where Support Vector Machine (RBF kernel) achieves the best performance (Accuracy = 0.821 ± 0.11) using four behavioral features.

We conclude that affectively controlled VR environments can serve as a viable paradigm for inferring Openness to Experience from behavioral interaction signals. These findings highlight the potential for implicit personality sensing in adaptive VR systems and personalized interactive environments.

Abstract

Digital twins (DTs) enable real-time monitoring, simulation, and predictive maintenance, but their use in computer networks remains limited. This paper presents the Access Point Digital Twin (APDT) framework, designed to address increasing client density and traffic congestion by tracking user behaviour and bandwidth demand, which directly affect network performance and Quality of Service (QoS) metrics such as latency and throughput. APDT captures the real-time state of the network using access point (AP) data, enabling simulation- based analysis and predictive modelling for evaluating what-if scenarios. We evaluated APDT on our University network using data collected from access points via Ruckus SmartZone API and incorporated NS-3 based simulations. The simulation replicates a real-time access point snapshots and models metrics such as latency and inter-packet transfer time. Additionally, a forecasting model predicts traffic congestion and suggests proactive client offloading, enhancing network management and performance optimisation. Preliminary results indicate that APDT can be used to predict short-term traffic surges, leading to improved QoS and reduced traffic congestion.

Abstract

Code-generating LLMs are increasingly used in software development, but compiling code does not imply functional correctness; outputs must be tested. This paper studies a lightweight QA strategy where unit tests are embedded in the prompt to guide code generation, followed by execution against a fixed test suite. Using the Mostly Basic Python Problems (MBPP) benchmark (964 problems, 3 assertions each), we evaluate twelve open-source LLMs with (i) plain prompts and (ii) assertionaugmented prompts, generating five samples per configuration and measuring pass@1 and pass@5. We find that including assertions often yields large correctness gains (frequently 20-30 points in pass@5), but the effect is model and problem-type dependent, with notable degradations for some instruction-tuned models. Across problem types, conditional-logic tasks are consistently easier, while recursion-heavy categories (e.g., dynamic programming/tree traversal) remain challenging. Based on these results, we outline SQA guidance on when test-driven prompting and limited resampling are effective for improving correctness in LLM-assisted programming.

Abstract

The rapid growth of Virtual Reality (VR) across critical sectors like healthcare, education, and gaming necessitates robust methods for quality assurance. As VR applications increase in complexity, ensuring correctness of interactions becomes challenging. This paper presents our ongoing work on a novel, cross-platform conformance checking system designed to verify that VR interactions are as specified. Our system features three key parts: a JSON-based format for defining ideal interaction specifications, an intuitive visual editor for designing the interaction flows, and a rules engine that automatically compares runtime behavior against the predefined sequences. This system is designed to streamline the testing process and ensure behavioral consistency across diverse VR platforms.

Abstract

Virtual Reality (VR) product requires expertise from diverse set of stakeholders. Moving from requirements to
design mock-up(s) while building a VR product is an iterative process and requires manual effort. Due to lack
of tool support, creating design templates and managing the respective versions turns out to be laborious and
difficult. In this paper, we describe VReqDV, a model-driven VR design generation and versioning tool that
can address this gap. The tool uses VR meta-model template as a foundation to facilitate a design pipeline.
VReqDV can potentially facilitate design generation, design viewing, design versioning, design to require-
ments conformity, traceability, and maintenance. It is a step forward in creating a Model-Driven Development
pipeline for VR scene design generation. We demonstrate the capabilities of VReqDV using a simple game
scene and share our insights for wider adoption by the VR community

Abstract

Software developers commonly use logging mechanisms to gather runtime data. Over the years, this information has been used for various purposes like debugging, behavioral analysis, system comprehension, etc. Frameworks such as log4j and Logback have helped standardize logging practices by incorporating Software Engineering principles. Logging interactions in Virtual Reality (VR) applications can help understand user behavior and assist with automated conformance checks. In this paper, we introduce VRSLOG, a generalized logging framework that can capture interactions across diverse VR scenes without any changes to the framework. We implement a prototype of the VRSLOG framework and demonstrate the generation of log files. Further, the log files are used to conduct conformance checks against a predefined expected sequence of events within the VR scene.

Abstract

Natural walking offers high immersion in Virtual
Reality (VR) but is constrained by physical space. Overlapping architectures, which reuse physical space, are a potential solution. Building on foundational principles of Architecturally Consistent Maze Generation in VR [1], we developed,implemented, and evaluated the Procedural Overlapping Maze System (POMS). POMS procedurally generates and prunes right-angled, overlapping maze corridors in real-time, enabling continuous natural walking in footprints ≥ 4x4m. This paper presents a controlled, between-group empirical evaluation (N=17 matched pairs) comparing POMS against a spatially equivalent static maze in an 8x8m tracked area using an Oculus Quest 3. Participants performed a time-constrained navigation task. We investigated effects on user engagement (time-on-task), usability (User Experience Questionnaire - UEQ), presence (igroup Presence Questionnaire - iPQ), and cybersickness (Simulator Sickness Questionnaire - SSQ). We hypothesized POMS would lead to increased time-on-task (H1), and that its dynamic nature would not negatively impact user experience, aiming for UEQ, iPQ, and SSQ outcomes comparable to a static environment (H2-H4). Results indicated no statistically significant differences in UEQ (H2) or iPQ (H3) between conditions, supporting our hypotheses that POMS could maintain these experiential qualities. Surprisingly, regarding cybersickness (H4), the increase in Oculomotor, Disorientation, and Total SSQ symptoms was significantly smaller in the POMS condition compared to the Static condition, while there was no significant difference for Nausea. This finding demonstrates an unexpected and substantial comfort benefit with POMS, exceeding our initial hypothesis of mere comparability. While POMS did not show a statistically significant increase in time-on-task (H1) in this study, a numerical trend suggesting longer engagement was observed. The markedly improved comfort achieved with POMS strongly suggests that such architectures have the potential to facilitate longer interaction periods, warranting further investigation into task design to fully leverage this benefit. These findings highlight that procedurally overlapping architectures like POMS can enable extended natural walking, not only without degrading core experiential qualities,but also by significantly enhancing user comfort.

Abstract

Developing Virtual Reality (VR) software products with discrete and incremental requirements is a challenging task for VR practitioners. A domain expert's assistance plays a key role in VR product completeness, as most VR requirements are abstract or under-specified during the early stages. Slight changes to the requirements can significantly impact the overall
flow of the VR software development process. In this paper, we introduce VReqST, a tool for VR requirement analysts to specify requirements for building effective VR software products. It is developed based on a Role-based model template for specifying virtual environments, custom behaviors, VR-specific algorithms, user-flows, action responses, timeline of events, etc. The tool has been developed after several interactions with VR practitioners from industry & academia. We share our insights on the effectiveness of this tool in practice.

Abstract

The rapid evolution of Large Language Models (LLMs) have opened new possibilities in automating tasks across the software developing life cycle, including test case generation This paper presents a comparative analysis of six LLMs in the context of generating test cases for technical requirements written in natural language (in this case English). We compare publicly available general purpose LLMs viz., BARD, ChatGPT3.5, Claude, Gemini, ChatGPT4.o (Omni) and Llama3. The generated test cases are tested against a Simulink model created for the corresponding set of requirements. The coverage metrics thus generated are used for a quantitative comparison of the LLMs.

Abstract

: Gathering requirements for developing virtual reality (VR) software
products is a labor-intensive process. It requires detailed elicitation of scene flow,
articles in the scene, action responses, custom behaviors, and timeline of events.
The slightest change in requirements will escalate the design and development
costs. While most VR practitioners depend on conventional software engineering
(SE) requirement-gathering techniques, there is a need for novel methods to
streamline VR software development. With severe software platform
fragmentation and hardware volatility, VR practitioners need assistance
specifying non-volatile requirements for a minimum viable VR software product.