The Order Statistic model of reliability growth offers improved• experimental design, and• flexibility in testing methodologycompared to conventional reliability growth models. It permits prediction of operational reliability without requiring that testing be conducted according to the operation profile of the program input space.This paper presents the first experimental use of the Order Statistic model under a test plan that combines both representative and directed tests. Results suggest that this is an effective way to obtain quantified measures of test quality without abandoning the advantages of directed test methods.

This paper provides an approach to test nonlinear functions in computer programs, whether this function is used for control flow, such as a predicate inequality or equality constraint, or is given as an input-output relationship. This approach will obtain test data to detect linear errors in the given nonlinear function. An error-space criterion previously given by Zeil will be utilized, and a necessary and sufficient condition for the test data will be specified to guarantee the satisfaction of this criterion. This leads to a simple and efficient method to select test data which satisfies that condition; only (n+2) tests are required, where n is the number of input variables. An analysis will be given to show that this simple approach can be very effective in detecting nonlinear errors as well.

Abstract Most testing methods generate test descriptions which define the desired characteristics of the input data in a test case. This paper describes the use of accumulated knowledge about a problem domain to refine these test descriptions, with the goal of increasing the probability that the input data generated from the refined test descriptions will reveal faults in a software system. A knowledge base is introduced to hold information about object semantics and object class/subclass relationships. Knowledge accumulates with experience in a particular domain and can be focused on those objects and relationships in that domain which experience has shown to be error‐prone. This paper also defines a knowledge‐driven functional testing (KDFT) method which derives test descriptions from a formal specification and refines these descriptions using that knowledge base. A case study of the KDFT method using data from a previous study of the launch intercept control problem is described. These preliminary results indicate that knowledge‐based refinement of test descriptions can dramatically improve their ability to detect certain classes of faults.

Domain testing attempts to find errors in the numeric expressions affecting the flow of control through a program. Intuitively, domain testing provides a systematic form of boundary value testing for the conditional statements within a program. Several forms of domain testing have been proposed, all dealing with the detection of linear errors in linear functions. Perturbation analysis has been previously developed as a measure of the volume of faults, from within a selected space of possible faults, left undetected by a test set. It is adapted here to errors and error spaces. The adapted form is used to show that the different forms of domain testing are closer in error detection ability than had been supposed. They may all be considered effective for finding linear errors in linear predicate functions. A simple extension is proposed, which allows them to detect linear errors in nonlinear predicate functions using only a single additional test point.

The authors report on the results of their evaluation of path-selection criteria based on data-flow relationships. They show how these criteria relate to each other, thereby demonstrating some of their strengths and weaknesses. A subsumption hierarchy showing their relationship is presented. It is shown that one of the major weaknesses of all the criteria is that they are based solely on syntactic information and do not consider semantic issues such as infeasible paths. The authors discuss the infeasible-path problem as well as other issues that must be considered in order to evaluate these criteria more meaningfully and to formulate a more effective path-selection criterion.>

Perturbation testing is an approach to software testing which focuses on faults within arithmetic expressions appearing throughout a program. This approach is expanded to permit analysis of individual test points rather than entire paths, and to concentrate on domain errors. Faults are modeled as perturbing functions drawn from a vector space of potential faults and added to the correct form of an arithmetic expression. Sensitivity measures are derived which limit the possible size of those faults that would go undetected after the execution of a given test set. These measures open up an interesting view of testing, in which attempts are made to reduce the volume of possible faults which, were they present in the program being tested, would have escaped detection on all tests performed so far. The combination of these measures with standard optimization techniques yields a novel test-data-generation method called arithmetic fault detection.>

Many testing methods require the selection of a set of paths on which tests are to be conducted. Errors in arithmetic expressions within program statements can be represented as perturbing functions added to the correct expression. It is then possible to derive the set of errors in a chosen functional class which cannot possibly be detected using a given test path. For example, test paths which pass through an assignment statement "X := f(Y)" are incapable of revealing if the expression "X -f( Y)" has been added to later statements. In general, there are an infinite number of such undetectable error perturbations for any test path. However, when the chosen functional class of error expressions is a vector space, a finite characterization of all undetectable expressions can be found for one test path, or for combined testing along several paths. An analysis of the undetected perturbations for sequential programs operating on integers and real numbers is presented which permits the detection of multinomial error terms. The reduction of the space of (potential undetected errors is proposed as a criterion for test path selection.