03-xx#

03-06 Process performance information
status: valid
tags: aspice40_iic03
links incoming: std_req__aspice_40__gp-324

Process performance information may have the following characteristics:

  • Measurements about defined quantitative or qualitative measurable indicators, that match defined information needs.

  • Measurement metrics for the calculation of the quantitatively or qualitatively measurable indicators

  • Data comparing process performance against expected levels

  • Examples for project performance information:

    • resource utilization against established target

    • time schedule against established target

    • activity or task completion criteria met

    • defined input and output work products available

    • process quality against quality expectations and/or criteria

    • product quality against quality expectations and/or criteria

    • highlight product performance issues, trends

  • Examples for service level performance information:

    • references any goals established

    • real time metrics related to aspects such as:

    • capacity

    • throughput

    • operational performance

    • operational service

    • service outage time

    • up time

    • job run time
std_req__aspice_40__iic-03-06
Standard Requirement
03-50 Verification Measure Data
status: valid
tags: aspice40_iic03
links incoming: std_req__aspice_40__SWE-4-BP3, std_req__aspice_40__SWE-5-BP5, std_req__aspice_40__SWE-6-BP3

Verification Measure Data may have the following characteristics:

  • Verification measure data are data recorded during the execution of a

    verification measure, e.g.:

    • for test cases: raw data, logs, traces, tool generated outputs

    • measurements: values

    • calculations: values

    • simulations: protocol

    • reviews such as optical inspections à findings record

    • analyses: values
std_req__aspice_40__iic-03-50
Standard Requirement
03-51 ML data set
status: valid
tags: aspice40_iic03
links incoming: std_req__aspice_40__MLE-3-BP2, std_req__aspice_40__MLE-4-BP2

  • Selection of ML Data for e.g., ML model training (ML Training and Validation Data Set) or test of the trained and deployed ML model (ML Test Data Set).
std_req__aspice_40__iic-03-51
Standard Requirement
03-53 ML data
status: valid
tags: aspice40_iic03
links incoming: std_req__aspice_40__SUP-11-BP3, std_req__aspice_40__SUP-11-BP4, std_req__aspice_40__SUP-11-BP5
  • Datum to be used for Machine Learning. The datum has to be attributed by metadata, e.g., unique ID and data characteristics. Examples:

    • Visual data like a photo or videos (but a video could also be considered as sequence of photos depending on the intended use)

    • Audio recording

    • Sensor data

    • Data created by an algorithm

    • Data might be processed to create additional data. E.g., processing could add noise, change colors or merge pictures.
std_req__aspice_40__iic-03-53
Standard Requirement