Generating better partial covering arrays by modeling weights on sub-product lines

Abstract

Combinatorial interaction testing is an approach for testing product lines. A set of products to test can be set up from the covering array generated from a feature model. The products occurring in a partial covering array, however, may not focus on the important feature interactions nor resemble any actual product in the market. Knowledge about which interactions are prevalent in the market can be modeled by assigning weights to sub-product lines. Such models enable a covering array generator to select important interactions to cover first for a partial covering array, enable it to construct products resembling those in the market and enable it to suggest simple changes to an existing set of products to test for incremental adaption to market changes. We report experiences from the application of weighted combinatorial interaction testing for test product selection on an industrial product line, TOMRA's Reverse Vending Machines. © 2012 Springer-Verlag.