While scientific workflows have been established and used in a number of disciplines for specifying and executing experiments and data analysis, early and recent studies have demonstrated that an important proportion of workflows suffer from decay. This phenomena is exacerbated by legacy scientific workflow systems, notably Taverna, which was popular in e-science for orchestrating complex analyses. A step towards addressing this issue, we report on in this paper a feasibility study on using generative AI to revive decayed workflows, combining large language models with modern workflow technologies. Our approach automates critical revival tasks including parsing of legacy Taverna workflows, failure point identification, repair suggestion, and conversion to contemporary formats, viz. SnakeMake. The methodology integrates AI-driven workflow summarization, pseudocode abstraction, graph-based visualization, automated service substitution, and code generation. We demonstrate and evaluate this approach through a real-world decayed workflow case study. We conclude the paper with a discussion on key lessons that we learned and will guide development of a systematic workflow revival framework as part of our future work. 
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                            Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow
                        
                    
    
            Generative AI brings novel and impressive abilities to help people in everyday tasks. There are many AI workflows that solve real and complex problems by chaining AI outputs together with human interaction. Although there is an undeniable lure of AI, it is uncertain how useful generative AI workflows are after the novelty wears off. Additionally, workflows built with generative AI have the potential to be easily customized to fit users’ individual needs, but do users take advantage of this? We conducted a three-week longitudinal study with 12 users to understand the familiarization and customization of generative AI tools for science communication. Our study revealed that there exists a familiarization phase, during which users were exploring the novel capabilities of the workflow and discovering which aspects they found useful. After this phase, users understood the workflow and were able to anticipate the outputs. Surprisingly after familiarization, the perceived utility of the system was rated higher than before, indicating that the perceived utility of AI is not just a novelty effect. The increase in benefits mainly comes from end-users’ ability to customize prompts, and thus potentially appropriate the system to their own needs. This points to a future where generative AI systems can allow us to design for appropriation. 
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                            - Award ID(s):
- 2129020
- PAR ID:
- 10543744
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705830
- Page Range / eLocation ID:
- 782 to 803
- Format(s):
- Medium: X
- Location:
- IT University of Copenhagen Denmark
- Sponsoring Org:
- National Science Foundation
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