Improving dynamic domain reduction test data generation method by Euler/Venn reasoning system

摘要

Abstract Test data adequacy is a major challenge in software testing literature. The difficulty is to provide sufficient test data to assure the correctness of the program under test. Especially, in the case of latent faults, the fault does not reveal itself unless specific combinations of input values are used to run the program. In this respect, detection of subdomains of the input domain that cover a specific execution path seems promising. A subdomain covers an execution path provided that each test data taken from the subdomain satisfies the path constraint. Dynamic Domain Reduction, or DDR in short, is a very well-known test data generation procedure, targeted at detection of the subdomains of the input domain that satisfy a given path constraint. In this paper, an improved version of DDR called Rapid Dynamic Domain Reduction, or RDDR in short, is introduced. RDDR is intended to explore subdomains of a program input domain, satisfying a given path constraint. For each feasible path, there is a distinct subdomain of the input domain that causes the program to execute the path. Hereby, we introduce a new metric named domain coverage, to qualify input data sets, in terms of the percentage of the subdomain of a feasible path covered by the data set. The main inspiration behind the domain coverage metric is to support test data adequacy. Our empirical results based on some well-known case studies confirms that RDDR significantly outperforms DDR in terms of speed and accuracy.