01-xx#

01-03 Software Component
status: valid
tags: aspice40_iic01
links incoming: std_req__aspice_40__SWE-5-BP4

Software Component may have the following characteristics:

  • Software element in the software architecture above the software unit level.

  • Represented by a design model element or executable code such as libs or scripts and a configuration description, if applicable.
std_req__aspice_40__iic-01-03
Standard Requirement
01-50 Integrated Software
status: valid
tags: aspice40_iic01
links incoming: std_req__aspice_40__SWE-5-BP4

Integrated Software may have the following characteristics:

  • Software executable (e.g, simulator with stubbing, debug-able, object code) including:

    • application parameter files (being a technical implementation solution for configurability-oriented requirements)

    • all configured software elements
std_req__aspice_40__iic-01-50
Standard Requirement
01-52 Configuration item list
status: valid
tags: aspice40_iic01
links incoming: std_req__aspice_40__SUP-8-BP1, std_req__aspice_40__SUP-8-BP2, std_req__aspice_40__SUP-8-BP7
complies to: wp__document_mgt_plan, wp__tlm_plan

Configuration item list may have the following characteristics:

  • Items under configuration control

  • The name of work products and an associated reference (to file, to tool artifact)

  • Configuration item attributes and properties
std_req__aspice_40__iic-01-52
Standard Requirement
01-53 Trained ML model
status: valid
tags: aspice40_iic01
links incoming: std_req__aspice_40__MLE-3-BP3

  • The trained ML model is the output of the training process. It consists of the software representing the ML architecture, the set of weights which were optimized during the training, and the final set of hyperparameters.
std_req__aspice_40__iic-01-53
Standard Requirement
01-54 Hyperparameter
status: valid
tags: aspice40_iic01
links incoming: std_req__aspice_40__MLE-2-BP1, std_req__aspice_40__MLE-2-BP2, std_req__aspice_40__MLE-3-BP3
  • Hyperparameters are used to control the ML model which has to be trained, e.g.:

    • Learn rate of training

    • Scaling of network (number of layers or neurons per layer)

    • Loss function

  • Minimum characteristics:

    • Description

    • Initial value

    • Final value upon communicating the results of the ML training
std_req__aspice_40__iic-01-54
Standard Requirement