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We propose noWorkflow, a tool that transparently captures provenance of scripts and enables reproducibility. Unlike existing approaches, noWorkflow is non-intrusive and does not require users to change the way they work -users need not wrap their experiments in scientific workflow systems, install version control systems, or instrument their scripts. The tool leverages Software Engineering techniques, such as abstract syntax tree analysis, reflection, and profiling, to collect different types of provenance, including detailed information about the underlying libraries. We describe how noWorkflow captures multiple kinds of provenance and the different classes of analyses it supports: graph-based visualization; differencing over provenance trails; and inference queries.

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Scripts are widely used to design and run scientific experiments. Scripting languages are easy to learn and use, and they allow complex tasks to be specified and executed in fewer steps than with traditional programming languages. However, they also have important limitations for reproducibility and data management. As experiments are iteratively refined, it is challenging to reason about each experiment run (or trial), to keep track of the association between trials and experiment instances as well as the differences across trials, and to connect results to specific input data and parameters. Approaches have been proposed that address these limitations by collecting, managing, and analyzing the provenance of scripts. In this article, we survey the state of the art in provenance for scripts. We have identified the approaches by following an exhaustive protocol of forward and backward literature snowballing. Based on a detailed study, we propose a taxonomy and classify the approaches using this taxonomy.