Chin-Yu Huang

A unified scheme of some Nonhomogenous Poisson process models for software reliability estimation

In this paper, we describe how several existing software reliability growth models based on Nonhomogeneous Poisson processes (NHPPs) can be comprehensively derived by applying the concept of weighted arithmetic, weighted geometric, or weighted harmonic mean. Furthermore, based on these three weighted means, we thus propose a more general NHPP model from the quasi arithmetic viewpoint. In addition to the above three means, we formulate a more general transformation that includes a parametric family of power transformations. Under this general framework, we verify the existing NHPP models and derive several new NHPP models. We show that these approaches cover a number of well-known models under different conditions.

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.

Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models

Software reliability is the probability of failure-free software operation for a specified period of time in a specified environment. During the last three decades, many software reliability growth models (SRGMs) have been proposed and analyzed for measuring software reliability growth. SRGMs are mathematical models that represent software failures as a random process and can be used to evaluate development status during testing. However, most of SRGMs depend on some assumptions or distributions. In this paper, we propose an artificial neural-network-based approach for software reliability estimation and modeling. We first explain the neural networks from the mathematical viewpoints of software reliability modeling. We will show how to apply neural network to predict software reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model (DWCM). The applicability of proposed model is demonstrated through real software failure data sets. The results obtained from the experiments show that the proposed model has a fairly accurate prediction capability.

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

Software Reliability Analysis by Considering Fault Dependency and Debugging Time Lag

Over the past 30 years, many software reliability growth models (SRGM) have been proposed. Often, it is assumed that detected faults are immediately corrected when mathematical models are developed. This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault, the skill and experience of personnel, the size of debugging team, the technique(s) being used, and so on. During software testing, practical experiences show that mutually independent faults can be directly detected and removed, but mutually dependent faults can be removed iff the leading faults have been removed. That is, dependent faults may not be immediately removed, and the fault removal process lags behind the fault detection process. In this paper, we will first give a review of fault detection & correction processes in software reliability modeling. We will then illustrate the fact that detected faults cannot be immediately corrected with several examples. We also discuss the software fault dependency in detail, and study how to incorporate both fault dependency and debugging time lag into software reliability modeling. The proposed models are fairly general models that cover a variety of known SRGM under different conditions. Numerical examples are presented, and the results show that the proposed framework to incorporate both fault dependency and debugging time lag for SRGM has a better prediction capability. In addition, an optimal software release policy for the proposed models, based on cost-reliability criterion, is proposed. The main purpose is to minimize the cost of software development when a desired reliability objective is given

Framework for modeling software reliability, using various testing-efforts and fault-detection rates

This paper proposes a new scheme for constructing software reliability growth models (SRGM) based on a nonhomogeneous Poisson process (NHPP). The main focus is to provide an efficient parametric decomposition method for software reliability modeling, which considers both testing efforts and fault detection rates (FDR). In general, the software fault detection/removal mechanisms depend on previously detected/removed faults and on how testing efforts are used. From practical field studies, it is likely that we can estimate the testing efforts consumption pattern and predict the trends of FDR. A set of time-variable, testing-effort-based FDR models were developed that have the inherent flexibility of capturing a wide range of possible fault detection trends: increasing, decreasing, and constant. This scheme has a flexible structure and can model a wide spectrum of software development environments, considering various testing efforts. The paper describes the FDR, which can be obtained from historical records of previous releases or other similar software projects, and incorporates the related testing activities into this new modeling approach. The applicability of our model and the related parametric decomposition methods are demonstrated through several real data sets from various software projects. The evaluation results show that the proposed framework to incorporate testing efforts and FDR for SRGM has a fairly accurate prediction capability and it depicts the real-life situation more faithfully. This technique can be applied to wide range of software systems.

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 30years, 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.

Analysis of a software reliability growth model with logistic testing-effort function

We investigate a software reliability growth model (SRGM) based on the Non Homogeneous Poisson Process (NHPP) which incorporates a logistic testing effort function. Software reliability growth models proposed in the literature incorporate the amount of testing effort spent on software testing which can be described by an exponential curve, a Rayleigh curve, or a Weibull curve. However it may not be reasonable to represent the consumption curve for testing effort only by an exponential, a Rayleigh or a Weibull curve in various software development environments. Therefore, we show that a logistic testing effort function can be expressed as a software development/test effort curve and give a reasonable predictive capability for the real failure data. Parameters are estimated and experiments on three actual test/debug data sets are illustrated. The results show that the software reliability growth model with logistic testing effort function can estimate the number of initial faults better than the model with Weibull type consumption curve. In addition, the optimal release policy of this model based on cost reliability criterion is discussed.