"I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development

• URL: http://arxiv.org/abs/2409.13343v2
• Date: Thu, 21 Nov 2024 07:17:57 GMT
• Title: "I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development
• Authors: Shidong Pan, Litian Wang, Tianyi Zhang, Zhenchang Xing, Yanjie Zhao, Qinghua Lu, Xiaoyu Sun,
• Abstract summary: This study investigates the adoption, impact, and security considerations of AI-empowered tools in the software development process. Our findings reveal widespread adoption of AI tools across various stages of software development.
• Score: 19.851794567529286
• License:
• Abstract: AI-empowered tools have emerged as a transformative force, fundamentally reshaping the software development industry and promising far-reaching impacts across diverse sectors. This study investigates the adoption, impact, and security considerations of AI-empowered tools in the software development process. Through semi-structured interviews with 19 software practitioners from diverse backgrounds, we explore three key aspects: the utility of AI tools, developers' attitudes towards them, and security and privacy responsibilities. Our findings reveal widespread adoption of AI tools across various stages of software development. Developers generally express positive attitudes towards AI, viewing it as an efficiency-enhancing assistant rather than a job replacement threat. However, they also recognized limitations in AI's ability to handle complex, unfamiliar, or highly specialized tasks in software development. Regarding security and privacy, we found varying levels of risk awareness among developers, with larger companies implementing more comprehensive risk management strategies. Our study provides insights into the current state of AI adoption in software development and offers recommendations for practitioners, organizations, AI providers, and regulatory bodies to effectively navigate the integration of AI in the software industry.

Related papers

• Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace [2.5280615594444567]
  Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beliefs. Our findings reveal that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable.
  arXiv  Detail & Related papers  (2024-10-24T00:07:27Z)
• The Design Space of in-IDE Human-AI Experience [6.05260196829912]
  Key findings stress the need for AI systems that are more personalized, proactive, and reliable. Our findings show that while Adopters appreciate advanced features and non-interruptive integration, Churners emphasize the need for improved reliability and privacy. Non-Users, in contrast, focus on skill development and ethical concerns as barriers to adoption.
  arXiv  Detail & Related papers  (2024-10-11T10:02:52Z)
• Future of Artificial Intelligence in Agile Software Development [0.0]
  AI can assist software development managers, software testers, and other team members by leveraging LLMs, GenAI models, and AI agents. AI has the potential to increase efficiency and reduce the risks encountered by the project management team.
  arXiv  Detail & Related papers  (2024-08-01T16:49:50Z)
• The Role of Generative AI in Software Development Productivity: A Pilot Case Study [0.0]
  This paper investigates the integration of generative AI tools within software development. Through a pilot case study, we gathered valuable experiences on the integration of generative AI tools into their daily work routines. Our findings reveal a generally positive perception of these tools in individual productivity while also highlighting the need to address identified limitations.
  arXiv  Detail & Related papers  (2024-06-01T21:51:33Z)
• Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns [23.867795468379743]
  Recent research has demonstrated that AI-generated code can contain security issues. How software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development.
  arXiv  Detail & Related papers  (2024-05-10T10:13:19Z)
• Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
  We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE. We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

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.