3 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
Monitoring Machine Learning Systems: A Multivocal Literature Review
Hira Naveed, Scott Barnett, Chetan Arora +3
Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating conte…
Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis
Yi Wang, Chetan Arora, Xiao Liu +4
Accessibility reviews provide valuable insights into both the limitations and benefits experienced by users with disabilities when using virtual reality (VR) applications. However,…
Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
Hira Naveed, John Grundy, Chetan Arora +2
Given the inherent non-deterministic nature of machine learning (ML) systems, their behavior in production environments can lead to unforeseen and potentially dangerous outcomes. F…
Managing Human-Centric Software Defects: Insights from GitHub and Practitioners' Perspectives
Vedant Chauhan, Chetan Arora, Hourieh Khalajzadeh +1
Context: Human-centric defects (HCDs) are nuanced and subjective defects that often occur due to end-user perceptions or differences, such as their genders, ages, cultures, languag…
Towards Standards-Compliant Assistive Technology Product Specifications via LLMs
Chetan Arora, John Grundy, Louise Puli +1
In the rapidly evolving field of assistive technology (AT), ensuring that products meet national and international standards is essential for user safety, efficacy, and accessibili…
Who Uses Personas in Requirements Engineering: The Practitioners' Perspective
Yi Wang, Chetan Arora, Xiao Liu +4
Personas are commonly used in software projects to gain a better understanding of end-users' needs. However, there is a limited understanding of their usage and effectiveness in pr…