Testing Effort Function Research Articles

Cost-reliability-optimal release policy for software reliability models incorporating improvements in testing efficiency

Over the past 30 years, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of products during software development processes. One of the most important applications of SRGMs is to determine the software release time. Most software developers and managers always want to know the date on which the desired reliability goal will be met. In this paper, we first review a SRGM with generalized logistic testing-effort function and the proposed generalized logistic testing-effort function can be used to describe the actual consumption of resources during the software development process. Secondly, if software developers want to detect more faults in practice, it is advisable to introduce new test techniques, tools, or consultants, etc. Consequently, here we propose a software cost model that can be used to formulate realistic total software cost projects and discuss the optimal release policy based on cost and reliability considering testing effort and efficiency. Some theorems and several numerical illustrations are also presented. Based on the proposed models and methods, we can specifically address the problem of how to decide when to stop testing and when to release software for use.

OPTIMAL ALLOCATION OF TESTING RESOURCE FOR A MODULAR SOFTWARE

Several Software Reliability Growth Models (SRGMs) have been developed in the literature to account for exponential and S-shaped growth curves. There are others, which can account for both depending on the testing environment. Such models are termed as flexible models. Most of the models use calendar/execution time as the testing time. Very few SRGMs have been developed which define explicitly the testing effort functions into the modeling. Testing effort/resource may be computer time and manpower needed during testing. The aim of this paper is twofold. 1. Develop an SRGM with testing efforts which is also flexible 2. Use model in (1) to allocate optimally the testing resource to a modular software subject to different constraints. Model developed in (1) is validated on different data sets and predictive validity is established. Optimization problems in (2) are mathematical programming problems having the sum of fractional functions as the common objective. These are solved using a dynamic programming approach and closed form solutions have been obtained. Finally, numerical illustrations are provided for two optimization problems.

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

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.

Analysis of incorporating logistic testing-effort function into software reliability modeling

This paper investigates a SRGM (software reliability growth model) based on the NHPP (nonhomogeneous Poisson process) which incorporates a logistic testing-effort function. SRGM proposed in the literature consider the amount of testing-effort spent on software testing which can be depicted as an exponential curve, a Rayleigh curve, or a Weibull curve. However, it might not be appropriate to represent the consumption curve for testing-effort by one of those curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be expressed as a software-development/test-effort curve and that it gives a good predictive capability based on real failure-data. Parameters are estimated, and experiments performed on actual test/debug data sets. Results from applications to a real data set are analyzed and compared with other existing models to show that the proposed model predicts better. In addition, an optimal software release policy for this model, based on cost-reliability criteria, is proposed.

Software reliability release policy with testing effort

An improved software reliability release policy is presented, based on the nonhomogeneous Poisson process (NHPP) and incorporating the effect of testing effort. Testing effort functions are modelled by exponential, Rayleigh and Weibull curves. The optimal software release time is determined by minimizing the total expected software cost under the conditions of satisfying a software reliability objective. Numerical examples have been included to illustrate the software release policy.