The impact of input domain reduction on search-based test data generation

Abstract

There has recently been a great deal of interest in search-based test data generation, with many local and global search algorithms being proposed. However, to date, there has been no investigation ofthe relationship between the size of the input domain (the search space) and performance of search-based algorithms. Static analysis can be used to remove irrelevant variables for a given test data generation problem, thereby reducing the search space size. This paper studies the effect of this domain reduction, presenting results from the application of local and global search algorithms to real world examples. This provides evidence to support the claimthat domain reduction has implications for practical search-based test data generation.