Abductive Reasoning and Automated Analysis of Feature Models: How are they connected?

Abstract

In the automated analysis feature models (AAFM), many operations have been defined to extract relevant information to be used on decision making. Most of the proposals rely on logics to give solution to different operations. This extraction of knowledge using logics is known as deductive reasoning. One of the most useful operations are explanations that provide the reasons why some other operations find no solution. However, explanations does not use deductive but abductive reasoning, a kind of reasoning that allows to obtain conjectures why things happen. As a first contribution we differentiate between deductive and abductive reasoning and show how this difference affect to AAFM. Secondly, we broaden the concept of explanations relying on abductive reasoning, applying them even when we obtain a positive response from other operations. Lastly, we propose a catalog of operations that use abduction to provide useful information.