The Minimal Failure-Causing Schema of Combinatorial Testing

Abstract

Combinatorial Testing (CT) involves the design of a small test suite to cover the parameter value combinations so as to detect failures triggered by the interactions among these parameters. To make full use of CT and to extend its advantages, this article first gives a model of CT and then presents a theory of the Minimal Failure-causing Schema (MFS), including the concept of the MFS, proof of its existence, some of its properties, and a method of finding the MFS. Then we propose a methodology for CT based on this MFS theory and the existing research. Our MFS-based methodology emphasizes that CT should work on accurate testing requirements, and has the following advantages: 1) Detect failure to the greatest degree with the least cost. 2) Effectiveness is improved by emphasizing mining of the information in software and making full use of the information gained from test design and execution. 3) Determine the root causes of failures and reveal related faults near the exposed ones. 4) Provide a foundation and model for regression testing and software quality evaluation of CT. A case study is presented to illustrate the MFS-based CT methodology, and an empirical study on a real software developed by us shows that the MFS really exists and the methodology based on MFS can considerably improve CT.