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Area of Science:

  • Computer Science
  • Data Science
  • Software Engineering

Background:

  • Data science code is often complex, unstructured, and poorly documented.
  • This lack of clarity hinders code comprehension by third parties.
  • The exploratory nature of data science leads to non-linear code, often called the 'garden of forking paths'.

Purpose of the Study:

  • To provide empirical evidence for the non-linearity of data science code.
  • To propose a novel visualization method to aid data science code interaction and comprehension.
  • To evaluate the impact of visualization and cell annotations on code comprehension.

Main Methods:

  • Collected empirical evidence from real-world Jupyter notebooks to confirm code non-linearity.
  • Developed a visualization method to elucidate implicit workflow information and pipeline steps.
  • Conducted a user experiment with data scientists to assess the effectiveness of the visualization and annotations.

Main Results:

  • Confirmed the non-linear nature of data science code, necessitating new comprehension approaches.
  • The proposed visualization method effectively aids navigation and understanding of complex code.
  • Visualizing the exploration process significantly improved users' overall code comprehension.

Conclusions:

  • Visualizing data science code exploration is crucial for improving comprehension.
  • The developed visualization method offers practical benefits for data scientists.
  • Further insights into data science code comprehension challenges were gained through qualitative analysis.