aws-samples / aws-lambda-docker-serverless-inference
Components & Dependencies

An overview of source code logical components.

Intro

Logical decomposition is a representation of the organization of the main source code, where every and each file is put in exactly one logical component.

  • A software system can have one or more logical decompositions.
  • A logical decomposition can be defined in two ways in Sokrates.
  • First approach is based on the folders structure. Components are mapped to folders at defined folder depth relative to the source code root.
  • Second approach is based on explicit definition of each component. In such explicit definitions, components are explicitly named and their files are selected based on explicitly defined path and content filters.
  • A logical decomposition is considered invalid if a file is selected into two or more components.This constraint is introduced in order to facilitate measuring of dependencies among components.
  • Files not assigned to any component are put into a special "Unclassified" component.
Learn more...
Logical Decompositions Overview

Analyzed system has 1 logical decomposition:

  • primary (12 components)

Logical Decomposition #1: PRIMARY

The decompositions is based on the folder structure (relative to the source code root), with automatically defined folder depth to have at least 10 components.

Bubble Chart | Tree Map
Components
The "primary" logical decomposition has 12 components.
  • 36 files, 877 lines of code (24.6% vs. main code).
  • "online-machine-learning-aws-lambda" is biggest, containing 20.3% of code.
  • "blazingtext-text-classification-train-in-sagemaker-deploy-with-lambda" is smallest, containing 0.91% of code.


online-machine-learning-aws-lambda178 LOC (4%) 6 files
xgboost-inference-arm64-docker-lambda115 LOC (3%) 4 files
djl-object-detection-inference-docker-lambda96 LOC (2%) 3 files
tensorflow-train-in-sagemaker-deploy-with-lambda88 LOC (2%) 3 files
djl-tensorflow-lite-inference-docker-lambda84 LOC (2%) 3 files
xgboost-inference-docker-lambda61 LOC (1%) 3 files
pytorch-inference-docker-lambda57 LOC (1%) 3 files
scikit-learn-inference-docker-lambda52 LOC (1%) 3 files
tensorflow-inference-docker-lambda50 LOC (1%) 2 files
hebert-sentiment-analysis-inference-docker-lambda45 LOC (1%) 3 files
xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda43 LOC (1%) 2 files
blazingtext-text-classification-train-in-sagemaker-deploy-with-lambda8 LOC (<1%) 1 files
Dependencies
Dependencies among components are static code dependencies among files in different components.

No component dependencies found.


2022-01-31 16:54