Pipeline Understanding Experiment

      • EX1.1 - Uniform documentation structure improves speed
      • EX1.2 - Quality of documentation of Jayvee and Python
      • EX1.3 - Documentation affects understanding
      • EX2.1 - Tool support affects understanding
      • EX2.2 - Community support
      • EX2.3 - Availability of third-party educational resources
      • HU1.1 - Python is harder to understand without previous experience
      • HU1.2 - Novices profit from good naming
      • HU1.3 - Good programming habits are required to keep Python easy to understand
      • HU1.4 - Less structure can lead to hard to understand implementation
      • HU2.1 - Mental model of an ETL pipeline aligns with Jayvee
      • HU2.2 - No learning effect between pipelines due to low structure
      • HU2.3 - Participants had previous experience with Python
      • HU2.4 - Participants had no experience with Jayvee and its language design
      • HU3.1 - Jayvee names describe functionality well
      • HU3.2 - Python function names are unclear or inconsistent
      • HU3.3 - Good names for steps is important to understanding
      • PL1.1 - Information is split between pipeline overview and implementation
      • PL1.2 - Without pipeline overview, all code must be considered
      • PL1.3 - Python does not provide a pipeline overview
      • PL1.4 - Jayvee pipeline overview improves speed
      • PL1.5 - Jayvee pipeline overview helps to understand the flow of a pipeline
      • PL2.1 - Sequential structure of Jayvee
      • PL2.2 - Blocks structure improves understanding
      • PL2.3 - Encapsulation of related code in blocks or functions improves understanding
      • PL2.4 - Jayvee enforces a structure
      • PL2.5 - No clearly defined or enforced structure in Python
      • PL3.1 - Hidden implementation details make understanding difficult
      • PL3.2 - Automation decreases understanding
      • PL3.3 - Interpretation blocks make outcome unclear
      • PL3.4 - Scoped code (blocks, functions) should execute one operation, not multiple
      • PL3.5 - Density of functionality effects understanding
      • PL4.1 - Amount of functionality Limited and General
      • PL4.2 - External libraries have different approaches and change
      • PL4.3 - Trade-off between customization and defaults
      • PL4.4 - Different functions to achieve the same goal decrease understanding
      • PL5.1 - Syntax is important for novices
      • PL5.2 - Unfamiliar syntax makes understanding difficult
      • PL5.3 - Code can be read like english text
      • PL5.4 - Expressive and verbose syntax
      • PL5.5 - Different syntax options for the same semantic decrease understanding
      • PL6.1 - Advanced programming concepts like map are hard to understand
      • PL6.2 - Domain-specific language elements like blocks make Jayvee easy to understand
      • PL6.3 - Jayvee language elements (blocks, value types, constraints) are unusual and need to be learned
      • PL6.4 - Meta infos (e.g., imports, library names) obscure the logic of the pipeline
      • PL6.5 - Mixed effects of no variables in Jayvee
        • EX - External Elements
        • EX1 - Documentation
        • EX2 - Ecosystem
        • HU - Human Factors
        • HU1 - Required Experience
        • HU2 - Applicable Experience
        • HU3 - Naming
        • PL - Programming Language
        • PL1 - Pipeline Overview
        • PL2 - Code Structure
        • PL3 - Transparency
        • PL4 - Amount of Options
        • PL5 - Syntax
        • PL6 - Language Elements
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    Themes (List)

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    HU - Human Factors

    HU - Human Factors

    Code Book

    Description

    Themes related to human factors of the data pipeline developer.

    Content

    • HU1 - Required Experience
    • HU2 - Applicable Experience
    • HU3 - Naming

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    • Description
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    • HU1 - Required Experience
    • HU2 - Applicable Experience
    • HU3 - Naming
    • Pipeline Understanding Experiment

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