Secure documentation quality in automated approval flows

Problem context
There is a need to increase the software release cadence but keep an acceptable level of documentation quality. One part in achieving this is by automating part of the approval process of the definition, and documentation of, diagnostic objects.
These objects are used to configure software logic, vehicle functionality and are also used in fault tracing operations. They are today mainly reviewed manually by various stakeholders within the company (like Aftermarket Technology, Manufacturing and System Engineering). These reviews can sometimes be very time and resource intensive


Description of thesis work
The goal of this thesis work is to investigate and develop a concept for how the documentation mentioned above could secure its data quality, in the form of wording, terminology use and technical setup.
It should strive to use a flexible solution which gives different stakeholders the possibility to tailor the automated approval process to their criteria or needs.
The work could include (e.g.)
Collection of information from various stakeholders such as review groups from Aftermarket Technology, Manufacturing, System Engineering or IT system owners
Investigate and identify which objects, based on certain criteria, can be part of the automated approval process.
Investigate various solutions to secure documentation quality. These could for example include, but not be limited to:
Machine learning
Implementing features for a more structured documentation.
Text prediction/suggestion functionality based on already defined terms.
Weighted scoring
Configuration functionality for approval criteria
Investigate and develop change requests/suggestions for multiple systems/applications based on the proposed solutions.
Develop a pilot/plugin to verify suggested solutions or concepts.

Educational background
Computer Science, Information Technology or similar


Thesis Level:
Master

Language:
English

Starting date:
Q4 2022/Q1 2023

Number of students:
1-2

Tutor
Markus Jonsson, Product Lead SEWS Approval Board, markus.jonsson@volvo.com