Failure size proportional models and an analysis of failure detection abilities of software testing strategies

This paper combines two distinct areas of research, namely software reliability growth modeling, and efficacy studies on software testing methods. It begins by proposing two software reliability growth models with a new approach to modeling. These models make the basic assumption that the intensity of failure occurrence during the testing phase of a piece of software is proportional to the s-expected probability of selecting a failure-causing input. The first model represents random testing, and the second model represents partition testing. These models provide the s-expected number of failures over a period, which in turn is used in analyzing the failure detection abilities of testing strategies. The specific areas of investigation are conditions that enable partition testing yielding optimal results, and comparison between partition testing and random testing in terms of efficacy.