Abstract
Combinatorial interaction testing (CIT) is an emerging testing technique that has proved to be effective in finding faults
due to the interaction among inputs. Efficient test generation for CIT is still an open problem especially when applied to
real models having meaningful size and containing many constraints among inputs. In this paper we present a novel technique
for the automatic generation of compact test suites starting from models containing constraints given in general form. It
is based on the use of Multivalued Decision Diagrams (MDDs) which prove to be suitable to efficiently support CIT. We devise
and experiment several optimizations including a novel variation of the classical greedy policy normally used in similar algorithms.
The results of a thorough comparison with other similar techniques are presented and show that our approach can provide several
advantages in terms of applicability, test suite size, generation time, and cost.
[read the copyright and download the pdf file] [DOI] [presentation] [The tool is available at https://github.com/garganti/medici]