arXiv subjects

Brandon Lit

Publications and source records attributed to Brandon Lit.

3 recordsLinked to original sources

arXiv2026

Exploring the Output of Software Testing Tools through a Visual Comparative Analysis

Software testing is a fundamental process of software development, and prior work has shown that visualizations of test results support testers' decision-making. However, Human-Computer Interaction research on software testing has yet to explore and understand the shared interface elements and patterns in visualization of testing outputs. To address this, we conducted a visual comparative analysis of the output of 50 software testing tools and harnesses (44 with CLI output, 6 with GUI output) across four popular programming languages. Our analysis reveals the common interface elements in software testing tools, how these tools display and visualize test results, as well as the specific make-up of the output. Our findings provide insight on how visual testing output is formatted and how colour is used across both CLI and GUI environments, identifying trends that can be applied by developers of testing tools.

cs.HC↗

arXiv2026

What is (H)CI: Why Does the "Human'' Matter?

Human-Computer Interaction (HCI) is a diverse field bringing together theories and methods from fields such as computer science, psychology, and human factors. Historically, HCI has focused on the human through ``user'' or ``human'' centered design, where the focus was either on information processing or understanding people and their concerns with respect to technology. However, amid the increasing adoption of generative AI tools, this workshop explores two critical questions in regards to HCI: What is HCI? and Why does the ``human'' matter? We aim to bring together researchers from diverse disciplines to reflect on these questions. Through guided discussions, group brainstorming, and reflection, we explore what HCI means, what the field may look like in the future, and why it is important to remember the ``human'' aspect of the field.

cs.HC↗

arXiv2025

"I know it's not right, but that's what it said to do": Investigating Trust in AI Chatbots for Cybersecurity Policy

AI chatbots are an emerging security attack vector, vulnerable to threats such as prompt injection, and rogue chatbot creation. When deployed in domains such as corporate security policy, they could be weaponized to deliver guidance that intentionally undermines system defenses. We investigate whether users can be tricked by a compromised AI chatbot in this scenario. A controlled study (N=15) asked participants to use a chatbot to complete security-related tasks. Without their knowledge, the chatbot was manipulated to give incorrect advice for some tasks. The results show how trust in AI chatbots is related to task familiarity, and confidence in their ownn judgment. Additionally, we discuss possible reasons why people do or do not trust AI chatbots in different scenarios.

cs.HC↗