Generating pairwise combinatorial test set using artificial parameters and values

Abstract

In order to meet market demands for quality software products, software engineers are increasingly under pressure to test more lines of codes. To maintain acceptable test coverage, software engineers need to consider a significantly large number of test set. Many combinations of possible input parameters, hardware/software environments, and system conditions need to be tested and verified against for conformance. Often, this results into combinatorial explosion problem (i.e. too many test data set too consider). Earlier work suggests that pairwise sampling strategy based on parameter interactions of variables can be effective. This paper discusses an efficient pairwise strategy, termed RA and ORA, that can systematically minimize the pairwise test set generated from higher order test parameters to lower order ones. In doing so, this paper demonstrates and compares the results against existing strategies including IRPS, IPO, GA, ACA, Jenny and All Pairs.