Logistic Testing-effort Function Research Articles

A Unified and Flexible Framework of Imperfect Debugging Dependent SRGMs with Testing-Effort

In order to overcome the limitations of debugging process, insufficient consideration of imperfect debugging and testing-effort (TE) in software reliability modeling and analysis, a software reliability growth model (SRGM) explicitly incorporating imperfect debugging and TE is developed. From the point of view of incomplete debugging and introduction of new fault, software testing process is described and a relatively unified SRGM framework is presented considering TE. The proposed framework models are fairly general models that cover a variety of the previous works on SRGM with ID and TE. Furthermore, a special SRGM incorporating an improved Logistic testing-effort function (TEF) into imperfect debugging modeling is proposed. The effectiveness and reasonableness of the proposed model are verified by published failure data set. The proposed model closer to real software testing has better descriptive and predictive power than other models

An Assessment of Testing-Effort Dependent Software Reliability Growth Models

Over the last several decades, many Software Reliability Growth Models (SRGM) have been developed to greatly facilitate engineers and managers in tracking and measuring the growth of reliability as software is being improved. However, some research work indicates that the delayed S-shaped model may not fit the software failure data well when the testing-effort spent on fault detection is not a constant. Thus, in this paper, we first review the logistic testing-effort function that can be used to describe the amount of testing-effort spent on software testing. We describe how to incorporate the logistic testing-effort function into both exponential-type, and S-shaped software reliability models. The proposed models are also discussed under both ideal, and imperfect debugging conditions. Results from applying the proposed models to two real data sets are discussed, and compared with other traditional SRGM to show that the proposed models can give better predictions, and that the logistic testing-effort function is suitable for incorporating directly into both exponential-type, and S-shaped software reliability models

Optimal Release Time for Software Systems Considering Cost, Testing-Effort, and Test Efficiency

In this paper, we study the impact of software testing effort & efficiency on the modeling of software reliability, including the cost for optimal release time. This paper presents two important issues in software reliability modeling & software reliability economics: testing effort, and efficiency. First, we propose a generalized logistic testing-effort function that enjoys the advantage of relating work profile more directly to the natural flow of software development, and can be used to describe the possible testing-effort patterns. Furthermore, we incorporate the generalized logistic testing-effort function into software reliability modeling, and evaluate its fault-prediction capability through several numerical experiments based on real data. Secondly, we address the effects of new testing techniques or tools for increasing the efficiency of software testing. Based on the proposed software reliability model, we present a software cost model to reflect the effectiveness of introducing new technologies. Numerical examples & related data analyzes are presented in detail. From the experimental results, we obtain a software economic policy which provides a comprehensive analysis of software based on cost & test efficiency. Moreover, the policy can also help project managers determine when to stop testing for market release at the right time.

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.

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.