Trust, transparency, and adoption in generative AI for software engineering: Insights from Twitter discourse

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology Pub Date : 2025-06-12 DOI:10.1016/j.infsof.2025.107804

Manaal Basha, Gema Rodríguez-Pérez

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引用次数: 0

Abstract

Context:

The rise of AI-driven coding assistants, such as GitHub Copilot and ChatGPT, are transforming software development practices. Despite their growing impact, informal user feedback on these tools is often neglected.

Objective:

This study aims to analyze Twitter/X conversations to understand user opinions on the benefits, challenges, and barriers associated with Code Generation Tools (CGTs) in software engineering. By incorporating diverse perspectives from developers, hobbyists, students, and critics, this research provides a comprehensive view of public sentiment.

Methods:

We employed a hybrid approach using BERTopic and open coding to collect and analyze data from approximately 90,000 tweets. The focus was on identifying themes and sentiments related to various CGTs. The study sought to determine the most frequently discussed topics and their related sentiment, followed by highlighting the reoccurring feedback or criticisms that could influence generative AI (GenAI) adoption in software engineering.

Results:

Our analysis identified several significant themes, including productivity enhancements, shifts in developer practices, regulatory uncertainty, and a demand for neutral GenAI content. While some users praised the efficiency benefits of CGTs, others raised concerns regarding intellectual property, transparency, and potential biases.

Conclusion:

The findings highlight that addressing issues of trust, accountability, and legal clarity is essential for the successful integration of CGTs in software development. These insights underscore the need for ongoing dialogue and refinement of CGTs to better align with user expectations and mitigate concerns.

查看原文本刊更多论文

软件工程生成式人工智能中的信任、透明度和采用：来自Twitter话语的见解

背景：人工智能驱动的编码助手（如GitHub Copilot和ChatGPT）的兴起正在改变软件开发实践。尽管它们的影响越来越大，但对这些工具的非正式用户反馈往往被忽视。目的：本研究旨在分析Twitter/X对话，以了解用户对软件工程中与代码生成工具（cgt）相关的好处、挑战和障碍的看法。通过整合来自开发者、业余爱好者、学生和评论家的不同观点，这项研究提供了一个全面的公众情绪视图。方法：我们采用BERTopic和开放编码的混合方法收集和分析了大约90,000条推文的数据。重点是确定与各种cgt相关的主题和情感。该研究试图确定最常讨论的话题及其相关情绪，然后强调可能影响软件工程中生成式人工智能（GenAI）采用的反复出现的反馈或批评。结果：我们的分析确定了几个重要的主题，包括生产力的提高、开发者实践的转变、监管的不确定性以及对中性GenAI内容的需求。虽然一些用户赞扬了cgt的效率效益，但也有人提出了对知识产权、透明度和潜在偏见的担忧。结论：研究结果强调了解决信任、责任和法律清晰度的问题对于软件开发中cgt的成功集成是必不可少的。这些见解强调了对cgt进行持续对话和改进的必要性，以便更好地与用户期望保持一致并减轻关注。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information and Software Technology 工程技术-计算机：软件工程

CiteScore

9.10

自引率

7.70%

发文量

164

审稿时长

9.6 weeks

期刊介绍： Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: • Software management, quality and metrics, • Software processes, • Software architecture, modelling, specification, design and programming • Functional and non-functional software requirements • Software testing and verification & validation • Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information. The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.