Comparison between the original code and AI generated code
In this review, three scripts that were written to analyse production data are compared. They are
the original script, AI generated script and an AI improved version of the original script. All the
scripts have codes for data importation, variable manipulation, descriptive statistical analysis,
and visualization. While they share the same goals of analysis, they significantly differ in
structure, function, and presentation.
The original code is sequential, employs the hardcoded dataset generated through pandas
dataframe and performs all operations, feature engineering, and visualization in the script's main
body. It computes new variables such as defects rate and units per hour based on simple
mathematical calculations, there is no modularity in the script. Plotting is all performed
sequentially using simple matplotlib functions with no reusable components. This structure is
appropriate for ad-hoc analysis, it gets in the way of scalability and makes the code less flexible
for more complex or repetitive tasks.
The AI generated code from scratch demonstrates a well-organized, modular approach. It splits
the entire workflow into functions for loading the data, feature engineering, descriptive statistics,
and plotting. Each function is properly labeled which makes reading easier and debugging more
straightforward. The script uses improved handling of missing data styling capabilities to
generate cleaner, more consistent visualizations. Labeling, titles and layout adjustments are
applied uniformly across plots, giving a professional, publication-ready appearance. The script
further enhances analysis by adding median and standard deviation to grouped statistics, giving
more insight into supervisor performance beyond averages.
The AI modified code maintains the original structure but refines it through formatting,
optimization, and clarity improvement. Variable naming becomes more consistent, redundant
code is slightly refactored, and plot is improved with figure size standardization, use of better
color schemes, and tighter layouts. The updated version does not rebuild the entire code but
optimizes it without changing its original form to the author. It's a balance between being
readable and being concise, and would be more suited to users who desire incremental
improvements over rewrites.
In this review, three scripts that were written to analyse production data are compared. They are
the original script, AI generated script and an AI improved version of the original script. All the
scripts have codes for data importation, variable manipulation, descriptive statistical analysis,
and visualization. While they share the same goals of analysis, they significantly differ in
structure, function, and presentation.
The original code is sequential, employs the hardcoded dataset generated through pandas
dataframe and performs all operations, feature engineering, and visualization in the script's main
body. It computes new variables such as defects rate and units per hour based on simple
mathematical calculations, there is no modularity in the script. Plotting is all performed
sequentially using simple matplotlib functions with no reusable components. This structure is
appropriate for ad-hoc analysis, it gets in the way of scalability and makes the code less flexible
for more complex or repetitive tasks.
The AI generated code from scratch demonstrates a well-organized, modular approach. It splits
the entire workflow into functions for loading the data, feature engineering, descriptive statistics,
and plotting. Each function is properly labeled which makes reading easier and debugging more
straightforward. The script uses improved handling of missing data styling capabilities to
generate cleaner, more consistent visualizations. Labeling, titles and layout adjustments are
applied uniformly across plots, giving a professional, publication-ready appearance. The script
further enhances analysis by adding median and standard deviation to grouped statistics, giving
more insight into supervisor performance beyond averages.
The AI modified code maintains the original structure but refines it through formatting,
optimization, and clarity improvement. Variable naming becomes more consistent, redundant
code is slightly refactored, and plot is improved with figure size standardization, use of better
color schemes, and tighter layouts. The updated version does not rebuild the entire code but
optimizes it without changing its original form to the author. It's a balance between being
readable and being concise, and would be more suited to users who desire incremental
improvements over rewrites.
