aws-samples / amazon-sagemaker-cv
Features of Interest
Aspects in the source code identified through RegEx patterns.
Intro
Features of interest are any aspects of a software system that can be identified through patterns in code.
Features of interest provide you with a way to focus your attention on relevant parts of the codebase.
Typical examples include, security, TODOs, logging.
A feature of interest may be present in multiple files. Any source code file may be in zero or multiple features of interest.
Overview
GENERAL
TODOs
8,658 LOC (38%)
54 files
|
found text
|
found text per file
1 GENERAL Concerns
The "general" group contains
2
concerns.
TODOs
Unclassified
general
1.1 TODOs
8,658 LOC (38%)
54 files
|
found text
|
found text per file
The following criteria are used to filter files:
files with any line of content like "
.*(TODO|FIXME)( |:| ).*
".
54
files match defined criteria (
8,658
lines of code,
38.0%
vs. main code):
43
*.py files (
5,738
lines of code)
7
*.cu files (
1,801
lines of code)
1
*.cpp files (
859
lines of code)
2
*.h files (
201
lines of code)
1
*.cuh files (
59
lines of code)
94
lines match the content pattern.
details...
per component - primary logical decomposition
pytorch/cuda_utils/smcv_utils
in 11 files, 2,920 LOC (58%)
pytorch/sagemakercv/detection
in 12 files, 1,671 LOC (61%)
tensorflow/sagemakercv/core
in 3 files, 818 LOC (34%)
pytorch/sagemakercv/utils
in 4 files, 375 LOC (21%)
tensorflow/sagemakercv/data
in 1 file 257 LOC (15%)
pytorch/sagemakercv/data
in 4 files, 865 LOC (55%)
tensorflow/sagemakercv/utils
in 2 files, 251 LOC (17%)
pytorch/sagemakercv/layers
in 6 files, 510 LOC (38%)
tensorflow/sagemakercv/training
in 3 files, 207 LOC (17%)
pytorch/sagemakercv/core
in 6 files, 700 LOC (73%)
pytorch/sagemakercv/training
in 1 file 46 LOC (8%)
pytorch/sagemakercv/inference
in 1 file 38 LOC (30%)