  Data workers usually seek to understand the semantics of data wrangling
scripts in various scenarios, such as code debugging, reusing, and maintaining.
However, the understanding is challenging for novice data workers due to the
variety of programming languages, functions, and parameters. Based on the
observation that differences between input and output tables highly relate to
the type of data transformation, we outline a design space including 103
characteristics to describe table differences. Then, we develop COMANTICS, a
three-step pipeline that automatically detects the semantics of data
transformation scripts. The first step focuses on the detection of table
differences for each line of wrangling code. Second, we incorporate a
characteristic-based component and a Siamese convolutional neural network-based
component for the detection of transformation types. Third, we derive the
parameters of each data transformation by employing a "slot filling" strategy.
We design experiments to evaluate the performance of COMANTICS. Further, we
assess its flexibility using three example applications in different domains.
