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When to stop testing is an important aspect in software test management. This paper discusses a new approach to this problem. There are two major improvements in this approach in comparison to the earlier studies. It uses failure size proportional model to estimate the number of failures and the corresponding cost of repair. Instead of reliability it uses a new measure called attained failure size. Failure size is the probability of finding a failure causing input in the input domain. Attained failure size is the failure size at the termination of testing. The advantages of these improvements are also discussed. [ABSTRACT FROM AUTHOR]

This paper discusses efficacy issues in software testing strategies through attained failure size. Failure size is the probability of finding an input that causes a failure in the input domain. As testing progresses, failure size decreases due to debugging. The failure size at the termination of testing is called the attained failure size. Using this measure, we compare the efficacies of partition testing and random testing, derive conditions that lead to the superiority of partition testing, and obtain optimal time allocations in partition testing. The core findings are presented in a decision tree to assist testers in test management. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]