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    Comantics automatically detects data transformation semantics by analyzing table differences and using a Siamese convolutional neural network. This aids data workers in understanding and managing complex wrangling scripts.

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    Area of Science:

    • Computer Science
    • Data Science
    • Software Engineering

    Background:

    • Understanding data wrangling script semantics is crucial for debugging, reuse, and maintenance.
    • Novice data workers face challenges due to diverse programming languages, functions, and parameters.
    • Table input-output differences correlate strongly with data transformation types.

    Purpose of the Study:

    • To develop an automated system, Comantics, for detecting data transformation script semantics.
    • To address the challenges faced by data workers in understanding complex wrangling processes.

    Main Methods:

    • Defined a design space with 103 characteristics to describe table differences.
    • Developed a three-step pipeline: detecting table differences, identifying transformation types using characteristic-based and Siamese CNN components, and filling transformation parameters.
    • Employed a "slot filling" strategy for parameter extraction.

    Main Results:

    • Comantics successfully detects the semantics of data transformation scripts.
    • Experimental evaluation demonstrated the system's performance.
    • Assessed flexibility across diverse application domains.

    Conclusions:

    • Comantics offers an effective solution for automatically understanding data transformation semantics.
    • The system enhances data wrangling script comprehension, aiding debugging, reuse, and maintenance.
    • Comantics shows promise for various data science applications.