Explainable and Transparent AI and Multi-Agent Systems

Abstract

Enterprise Resource Planning (ERP) software is used by businesses and extended via customisation. Automated custom code analysis and migration is a critical issue at ERP release upgrade times. Despite research advances, automated code analysis and transformation require a huge amount of manual work related to parser adaptation, rule extension and post-processing. These operations become unmanageable if the frequency of updates increases from yearly to monthly intervals. This article describes how the process of custom code analysis to custom code transformation can be automated in an explainable way. We develop an aggregate taxonomy for explainability and analyse the requirements based on roles. We explain in which steps on the new code migration process machine learning is used. Further, we analyse additional effort needed to make the new way of code migration explainable to different stakeholders.