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When a new computer software package is developed, a testing procedure is often put into effect to eliminate the faults, or bugs, in the package. One common procedure is th try the package on a set of well-known problems to try to see if any errors result. This goes for some fixed time with all detected errors being noted. Then the testing stops and the package is carefully checked to determine the r specific bugs that were responsible for the observed errors, and the package is then altered to remove these bugs. A problem of great importance is the estimation of the error rate of this revised software package. To model the above, we suppose that initially the package contains m, an unknown number of bugs which cause errors to occur in accordance with independent Poisson process having unknown rates λi, i = 1, …, m. We suppose that the package is to be run for t time units and that each error is, independently, detected with some known probability p. At the end of this time, a careful check of the package is made to determine the specific bugs that caused the detected errors (that is, a debugging takes place). These bugs are then removed and the problem of interest is to determine the error rate for the revised package. In this paper, we show how to estimate this quantity under a variety of assumptions as to what is learned when the debugging occurs. [ABSTRACT FROM AUTHOR]

A hardware failure analysis technique adapted to software yielded three rules for generating test cases sensitive to code errors. These rules, and a procedure for generating these cases, are given with examples. Areas for further study are recommended. [ABSTRACT FROM AUTHOR]

The theory of test data selection proposed by Goodenough and Gerhart is examined. In order to extend and refine this theory, the concepts of a revealing test criterion and a revealing subdomain are proposed. These notions are then used to provide a basis for constructing program tests. A subset of a program's input domain is revealing if the existence of one incorrectly processed input implies that all of the subset's elements axe processed incorrectly. The intent of this notion is to partition the program's domain in such a way that all elements of an equivalence class are either processed correctly or incorrectly. A test set is then formed by choosing one element from each class. This process represents perfect program testing. For a practical testing strategy, the domain is partitioned into subdomains which are revealing for errors considered likely to occur. Three programs which have previously appeared in the literature are discussed and tested using the notions developed in the paper. [ABSTRACT FROM AUTHOR]

This paper presents a testing strategy designed to detect errors in the control flow of a computer program, and the conditions under which this strategy is reliable are given and characterized. The control flow statements in a computer program partition the input space into a set of mutually exclusive domains, each of which corresponds to a particular program path and consists of input data points which cause that path to be executed. The testing strategy generates test points to examine the boundaries of a domain to detect whether a domain error has occurred, as either one or more of these boundaries will have shifted or else the corresponding predicate relational operator has changed. If test points can be chosen within ∈ of each boundary, under the appropriate assumptions, the strategy is shown to be reliable in detecting domain errors of magnitude greater than ∈. Moreover, the number of test points required to test each domain grows only linearly with both the dimensionality of the input space and the number of predicates along the path being tested. [ABSTRACT FROM AUTHOR]