Development in times of hype: How freelancers explore Generative AI?
- URL: http://arxiv.org/abs/2401.05790v1
- Date: Thu, 11 Jan 2024 09:49:50 GMT
- Title: Development in times of hype: How freelancers explore Generative AI?
- Authors: Mateusz Dolata, Norbert Lange, Gerhard Schwabe
- Abstract summary: Generative AI presents unique challenges to developers who have not previously engaged with it.
We identify multiple challenges associated with developing solutions based on generative AI.
We propose Software Engineering for Generative AI (SE4GenAI) and Hype-Induced Software Engineering (HypeSE) as areas where the software engineering community can provide effective guidance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The rise of generative AI has led many companies to hire freelancers to
harness its potential. However, this technology presents unique challenges to
developers who have not previously engaged with it. Freelancers may find these
challenges daunting due to the absence of organizational support and their
reliance on positive client feedback. In a study involving 52 freelance
developers, we identified multiple challenges associated with developing
solutions based on generative AI. Freelancers often struggle with aspects they
perceive as unique to generative AI such as unpredictability of its output, the
occurrence of hallucinations, and the inconsistent effort required due to
trial-and-error prompting cycles. Further, the limitations of specific
frameworks, such as token limits and long response times, add to the
complexity. Hype-related issues, such as inflated client expectations and a
rapidly evolving technological ecosystem, further exacerbate the difficulties.
To address these issues, we propose Software Engineering for Generative AI
(SE4GenAI) and Hype-Induced Software Engineering (HypeSE) as areas where the
software engineering community can provide effective guidance. This support is
essential for freelancers working with generative AI and other emerging
technologies.
Related papers
- Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins.
The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
arXiv Detail & Related papers (2024-10-28T13:03:09Z) - Making sense of AI systems development [3.6141428739228894]
We describe challenges in modern AI-based systems development that emerged in projects carried out by IBM and client companies.
Many issues bear upon the current-generation AI's inherent characteristics.
Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.
arXiv Detail & Related papers (2024-08-08T08:46:32Z) - 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) - 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) - On the Challenges and Opportunities in Generative AI [135.2754367149689]
We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains.
In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
arXiv Detail & Related papers (2024-02-28T15:19:33Z) - Exploring the intersection of Generative AI and Software Development [0.0]
The synergy between generative AI and Software Engineering emerges as a transformative frontier.
This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development.
It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
arXiv Detail & Related papers (2023-12-21T19:23:23Z) - Building Your Own Product Copilot: Challenges, Opportunities, and Needs [16.710056957807353]
We interviewed 26 professional software engineers responsible for building product copilots at various companies.
We found pain points at every step of the engineering process and the challenges that strained existing development practices.
arXiv Detail & Related papers (2023-12-21T18:37:43Z) - Embedded Software Development with Digital Twins: Specific Requirements
for Small and Medium-Sized Enterprises [55.57032418885258]
Digital twins have the potential for cost-effective software development and maintenance strategies.
We interviewed SMEs about their current development processes.
First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach.
arXiv Detail & Related papers (2023-09-17T08:56:36Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - 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) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
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.