Presentation
Towards Generating Contracts for Scientific Data Analysis Workflows
DescriptionTo increase the dependability and portability of scientific data analysis workflows (DAWs), recent work has proposed contract-driven design of DAWs, providing verifiable expectations and obligations to ensure that tasks run in a proper environment and produce correct results.
However, the specification of suitable contracts is still left to the discretion of DAW developers, imposing labor-intensive manual work which likely hampers the widespread adoption of contracts in scientific practice. We report about work-in-progress of developing a pipeline empowered by Large Language Models for automatically generating code contracts from logical workflow descriptions. We instantiate this pipeline within the workflow system Nextflow, and evaluate its contract generation capabilities in an experiment using real-world Nextflow modules. Our findings indicate that we generate a substantial amount of contracts serving as starting point for DAW developers. Our approach demonstrates potential in assisting domain scientists with contract-driven design of DAWs, laying the groundwork for its future adoption.
However, the specification of suitable contracts is still left to the discretion of DAW developers, imposing labor-intensive manual work which likely hampers the widespread adoption of contracts in scientific practice. We report about work-in-progress of developing a pipeline empowered by Large Language Models for automatically generating code contracts from logical workflow descriptions. We instantiate this pipeline within the workflow system Nextflow, and evaluate its contract generation capabilities in an experiment using real-world Nextflow modules. Our findings indicate that we generate a substantial amount of contracts serving as starting point for DAW developers. Our approach demonstrates potential in assisting domain scientists with contract-driven design of DAWs, laying the groundwork for its future adoption.