A Multi-Objective Genetic Algorithm for Location in Interaction Testing

Software testing is a key component of the software engineering process, but modern software is highly complex. Software configurations involve many interacting components and interactions among them can strongly affect the software's behavior in hard-to-predict ways. Combinatorial interaction testing (CIT) concerns the creation of test suites that either detect or locate the most important interactions in a large scale software system. Locating Arrays (LAs) are a data structure that guarantees a unique location for every such set of interactions. In this paper we present LocAG, an algorithm that generates LAs. Our approach uses a simple but powerful "partitioning" method of interactions to greatly reduce the computational cost of verifying a candidate LA. Further, we use evolutionary computation to quickly determine any additional tests after the partitioning method is complete. We are able to generate LAs for larger systems faster, with any desired separation, and greater interaction size than any existing approach.