Critical business applications require reliable software, but developing reliable software is one of the most difficult problems facing the software industry After the software is shipped, software vendors receive customer feedback about software reliability However, by then it is too late; software vendors need to know whether their products are reliable before they are delivered to customers Software reliability growth models help provide that information Unfortunately, very little real data and models from commercial applications have been published, possibly because of the proprietary nature of the data Over the past few years, the author and his colleagues at Tandem have experimented with software reliability growth models At Tandem, a major software release consists of substantial modifications to many products and may contain several million lines of code Major software releases follow a well defined development process and involve a coordinated quality assurance effort We applied software reliability modeling to a subset of products for four major releases The article reports on what was learned

Predicting software reliability

The resource allocation problem seeks to find an optimal allocation of a fixed amount of resources to activities so as to minimize the cost incurred by the allocation. A simplest form of the problem is to minimize a separable convex function under a single constraint concerning the total amount of resources to be allocated. The amount of resources to be allocated to each activity is treated as a continuous or integer variable, depending on the cases. This can be viewed as a special case of the nonlinear programming problem or the nonlinear integer programming problem.

Resource Allocation Problems

A general software reliability model based on the nonhomogeneous Poisson process (NHPP) is used to derive a model that integrates imperfect debugging with the learning phenomenon. Learning occurs if testing appears to improve dynamically in efficiency as one progresses through a testing phase. Learning usually manifests itself as a changing fault-detection rate. Published models and empirical data suggest that efficiency growth due to learning can follow many growth-curves, from linear to that described by the logistic function. On the other hand, some recent work indicates that in a real industrial resource-constrained environment, very little actual learning might occur because nonoperational profiles used to generate test and business models can prevent the learning. When that happens, the testing efficiency can still change when an explicit change in testing strategy occurs, or it can change as a result of the structural profile of the code under test and test-case ordering.

A general imperfect-software-debugging model with S-shaped fault-detection rate

NHPP software reliability and cost models with testing coverage

In this paper, a scheme for constructing software reliability growth model based on Non-Homogeneous Poisson Process is proposed. The main focus is to provide a method for software reliability modeling, which considers both testing-effort and change-point. In the vast literature, most researchers assume a constant detection rate per fault in deriving their software reliability models. They suppose that all faults have equal probability of being detected during the software testing process, and the rate remains constant over the intervals between fault occurrences. In reality, the fault detection rate strongly depends on the skill of test teams, program size, and software testability. Therefore, it may not be smooth and can be changed. On the other hand, sometimes we have to detect more additional faults in order to reach the desired reliability objective during testing. It is advisable for project managers to purchase new automated test tool, technology or additional manpower. These approaches can provide a conspicuous improvement in software testing and productivity. In this case, the fault detection rate will be changed during the software development process. Therefore, here we incorporate both generalized logistic testing-effort function and change-point parameter into software reliability modeling. New theorems are proposed and software testing data collected from real application are utilized to illustrate the proposed model. Experimental results show that the proposed framework to incorporate both testing-effort and change-point for SRGM has a fairly accurate prediction capability.

Performance analysis of software reliability growth models with testing-effort and change-point

Many software reliability growth models have been proposed in the past decade. Those models tacitly assume that testing-effort expenditures are constant throughout software testing. This paper develops realistic software reliability growth models incorporating the effect of testing-effort. The software error detection phenomenon in software testing is modeled by a nonhomogeneous Poisson process. The software reliability assessment measures and the estimation methods of parameters are investigated. Testing-effort expenditures are described by exponential and Rayleigh curves. Least-squares estimators and maximum likelihood estimators are used for the reliability growth parameters. The software reliability data analyses use actual data. The software reliability growth models with testing-effort can consider the relationship between the software reliability growth and the effect of testing-effort. Thus, the proposed models will enable us to evaluate software reliability more realistically.

Software Reliability Growth Models with Testing-Effort

Two kinds of software-testing management problems are considered: testing-resource allocation to best use specified testing resources during module testing, and a testing-resource control problem concerning how to spend the allocated amount of testing-resource expenditures during it. A software reliability growth model based on a nonhomogeneous Poisson process is introduced. The model describes the time-dependent behavior of software errors detected and testing-resource expenditures spent during the testing. The optimal allocation and control of testing resources among software modules can improve reliability and shorten the testing stage. Based on the model, numerical examples of these two software testing management problems are presented. >

Optimal allocation and control problems for software-testing resources

Software reliability growth models for testing-effort control

An optimal software-release problem of determining the time to stop testing and release the software system to the user is discussed. The optimality considers software error detection during not only the testing phase but also the operation phase. Two evaluation criteria for the problem are used: software reliability and mean time between failures. Numerical examples illustrate the optimal software-release problem. >

Optimum software-release time considering an error-detection phenomenon during operation

A testing-effort dependent software reliability model and its application

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