The Dilemma of Building Do-It-Yourself (DIY) Solutions for Workplace Accessibility
- URL: http://arxiv.org/abs/2501.18148v1
- Date: Thu, 30 Jan 2025 05:30:35 GMT
- Title: The Dilemma of Building Do-It-Yourself (DIY) Solutions for Workplace Accessibility
- Authors: Yoonha Cha, Victoria Jackson, Karina Kohl, Rafael Prikladnicki, André van der Hoek, Stacy M. Branham,
- Abstract summary: Existing commercial and in-house software development tools are often inaccessible to Blind and Low Vision Software Professionals (BLVSPs)
We shed light on the currently unexplored intersection of how DIY tools built and used by BLVSPs support accessible software development.
- Score: 10.287777207207352
- License:
- Abstract: Existing commercial and in-house software development tools are often inaccessible to Blind and Low Vision Software Professionals (BLVSPs), hindering their participation and career growth at work. Building on existing research on Do-It-Yourself (DIY) Assistive Technologies and customized tools made by programmers, we shed light on the currently unexplored intersection of how DIY tools built and used by BLVSPs support accessible software development. Through semi-structured interviews with 30 BLVSPs, we found that such tools serve many different purposes and are driven by motivations such as desiring to maintain a professional image and a sense of dignity at work. These tools had significant impacts on workplace accessibility and revealed a need for a more centralized community for sharing tools, tips, and tricks. Based on our findings, we introduce the "Double Hacker Dilemma" and highlight a need for developing more effective peer and organizational platforms that support DIY tool sharing.
Related papers
- Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
We propose MeCo, an adaptive decision-making strategy for external tool use.
MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools.
Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
arXiv Detail & Related papers (2025-02-18T15:45:01Z) - Development and Adoption of SATD Detection Tools: A State-of-practice Report [5.670597842524448]
Self-Admitted Technical Debt (SATD) refers to instances where developers knowingly introduce suboptimal solutions into code.
This paper provides a comprehensive state-of-practice report on the development and adoption of SATD detection tools.
arXiv Detail & Related papers (2024-12-18T12:06:53Z) - Information Seeking Using AI Assistants [9.887133861477233]
We conducted a mixed-method study to understand AI-assisted information seeking behavior of practitioners.
We found that developers are increasingly using AI tools to support their information seeking, citing increased efficiency as a key benefit.
Our efforts have implications for effective integration of AI tools into developer as information retrieval and learning aids.
arXiv Detail & Related papers (2024-08-07T18:27:13Z) - Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models.
Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions.
We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
arXiv Detail & Related papers (2024-05-25T06:41:23Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update [69.59482029810198]
CLOVA is a Closed-Loop Visual Assistant that operates within a framework encompassing inference, reflection, and learning phases.
Results demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing.
arXiv Detail & Related papers (2023-12-18T03:34:07Z) - Good Tools are Half the Work: Tool Usage in Deep Learning Projects [5.966029067108828]
The rising popularity of deep learning (DL) methods and techniques has invigorated interest in the topic of SE4DL (Software Engineering for Deep Learning)
About 63% of the GitHub repositories we examined contained at least one conventional SE tool.
Software construction tools are the most widely adopted, while the opposite applies to management and maintenance tools.
arXiv Detail & Related papers (2023-10-29T19:21:33Z) - Developing A Personal Decision Support Tool for Hospital Capacity
Assessment and Querying [0.0]
This article showcases a personal decision support tool (PDST) called HOPLITE, for performing insightful and actionable quantitative assessments of hospital capacity.
The results of extensive development and testing indicate that HOPLITE can automate many nuanced tasks.
The functionality that HOPLITE provides may make it easier to calibrate hospitals strategically and/or tactically to demands.
arXiv Detail & Related papers (2023-07-31T22:51:44Z) - Large Language Models as Tool Makers [85.00361145117293]
We introduce a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.
Our approach consists of two phases: 1) tool making: an LLM acts as the tool maker that crafts tools for a set of tasks. 2) tool using: another LLM acts as the tool user, which applies the tool built by the tool maker for problem-solving.
arXiv Detail & Related papers (2023-05-26T17:50:11Z) - Making Language Models Better Tool Learners with Execution Feedback [36.30542737293863]
Tools serve as pivotal interfaces that enable humans to understand and reshape the environment.
Existing tool learning methodologies induce large language models to utilize tools indiscriminately.
We propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution.
arXiv Detail & Related papers (2023-05-22T14:37:05Z) - Tool Learning with Foundation Models [158.8640687353623]
With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans.
Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
arXiv Detail & Related papers (2023-04-17T15:16:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.