Jung-Hua Lo

An integration of fault detection and correction processes in software reliability analysis

Software reliability is defined as the probability of failure-free software operation for a specified period of time in a specified environment and is widely recognized as one of the most significant aspects of software quality. Over the past 30 years, many software reliability growth models (SRGMs) have been proposed and they can greatly help us to estimate some important measures such as the mean time to failure, the number of remaining faults, defect levels, and the failure intensity, etc. Besides, SRGMs can also help to determine person power needed to support the desired reliability requirements. However, from our studies, most of SRGMs only focus on describing the behavior of fault detection process and assume that faults are fixed immediately upon detection. In fact, this assumption may not be realistic. Thus, in this paper, we will propose a general framework for modeling the software fault detection and correction processes. We will also show that the proposed approaches cover a number of well-known SRGMs. Two numerical examples based on two real software failure data sets are presented and discussed in detail.

Optimal resource allocation for cost and reliability of modular software systems in the testing phase

Reliability is one of the most important quality attributes of commercial software since it quantifies software failures during the development process. In order to increase the reliability, we should have a comprehensive test plan that ensures all requirements are included and tested. In practice, software testing must be completed within a limited time and project managers should know how to allocate the specified testing-resources among all the modules. In this paper, we present an optimal resource allocation problem in modular software systems during testing phase. The main purpose is to minimize the cost of software development when the fixed amount of testing-effort and a desired reliability objective are given. An elaborated optimization algorithm based on the Lagrange multiplier method is proposed and numerical examples are illustrated. Moreover, sensitivity analysis is also conducted. We analyze the sensitivity of parameters of proposed software reliability growth models and show the results in detail. The experimental results greatly help us to identify the contributions of each selected parameter and its weight. The proposed algorithm and method can facilitate the allocation of limited testing-resource efficiently and thus the desired reliability objective during software module testing can be better achieved.

Optimal resource allocation and reliability analysis for component-based software applications

In this paper we propose an analytical approach for estimating the reliability of a component-based software. This methodology assumes that the software components are heterogeneous and the transfers of control between components follow a discrete time Markov process. Besides, we also formulate and. solve two resource allocation problems. Finally, we demonstrate how these analytical approaches can be employed to measure the reliability of a software system including multiple-input/multiple-output systems and distributed software systems. Experimental results show that the proposed methods can solve the testing-effort allocation problems and improve the quality and reliability of a software system.

Optimal allocation of testing-resource considering cost, reliability, and testing-effort

We investigate an optimal resource allocation problem in modular software systems during testing phase. The main purpose is to minimize the cost of software development when the number of remaining faults and a desired reliability objective are given. An elaborated optimization algorithm based on the Lagrange multiplier method is proposed and numerical examples are illustrated. Besides, sensitivity analysis is also conducted. We analyze the sensitivity of parameters of proposed software reliability growth models and show the results in detail. In addition, we present the impact on the resource allocation problem if some parameters are either overestimated or underestimated. We can evaluate the optimal resource allocation problems for various conditions by examining the behavior of the parameters with the most significant influence. The experimental results greatly help us to identify the contributions of each selected parameter and its weight. The proposed algorithm and method can facilitate the allocation of limited testing-resource efficiently and thus the desired reliability objective during software module testing can be better achieved.

Software reliability modeling and cost estimation incorporating testing-effort and efficiency

Many studies have been performed on the subject of software reliability but few have explicitly considered the impact of software testing on the reliability process. This paper presents two important issues on software reliability modeling and software reliability economics: testing effort and efficiency. First, we discuss on how to extend the logistic testing-effort function into a general form. The generalized logistic testing-effort function has the advantage of relating the work profile more directly to the natural flow of software development. Therefore, it can be used to describe the actual consumption of resources during the software development process and to obtain a conspicuous improvement in modeling testing-effort expenditures. Furthermore, we incorporate the generalized logistic testing-effort function into software reliability modeling and its fault-prediction capability is evaluated through four numerical experiments on real data. Then, we address the effects of automated techniques or tools on increasing the efficiency of software testing. New testing techniques usually increase test coverage. We propose a modified software reliability cost model to reflect these effects. From the simulation results, we obtain a powerful software economic policy which clearly indicates the benefits of applying new automated testing techniques and tools during the software development process.

Sensitivity analysis of software reliability for component-based software applications

The parameters in these software reliability models are usually directly obtained from the field failure data. Due to the dynamic properties of the system and the insufficiency of the failure data, the accurate values of the parameters are hard to determine. Therefore, the sensitivity analysis is often used in this stage to deal with this problem. Sensitivity analysis provides a way to analyzing the impact of the different parameters. In order to assess the reliability of a component-based software, we propose a new approach to analyzing the reliability of the system, based on the reliabilities of the individual components and the architecture of the system. Furthermore, we present the sensitivity analysis on the reliability of a component-based software in order to determine which of the components affects the reliability of the system most. Finally, three general examples are evaluated to validate and show the effectiveness of the proposed approach.

Quantitative software reliability modeling from testing to operation

We first describe how several existing software reliability growth models based on nonhomogeneous Poisson processes (NHPPs) can be derived based on a unified theory for NHPP models. Under this general framework, we can verify existing NHPP models and derive new NHPP models. The approach covers a number of known models under different conditions. Based on these approaches, we show a method of estimating and computing software reliability growth during the operational phase. We can use this method to describe the transitions from the testing phase to operational phase. That is, we propose a method of predicting the fault detection rate to reflect changes in the users operational environments. The proposed method offers a quantitative analysis on software failure behavior in field operation and provides useful feedback information to the development process.

Optimal allocation of testing resources for modular software systems

In this paper, based on software reliability growth models with generalized logistic testing-effort function, we study three optimal resource allocation problems in modular software systems during the testing phase: 1) minimization of the remaining faults when a fixed amount of testing-effort and a desired reliability objective are given; 2) minimization of the required amount of testing-effort when a specific number of remaining faults and a desired reliability objective are given; and 3) minimization of the cost when the number of remaining faults and a desired reliability objective are given. Several useful optimization algorithms based on the Lagrange multiplier method are proposed and numerical examples are illustrated. Our methodologies provide practical approaches to the optimization of testing-resource allocation with a reliability objective. In addition, we also introduce the testing-resource control problem and compare different resource allocation methods. Finally, we demonstrate how these analytical approaches can be employed in the integration testing. Using the proposed algorithms, project managers can allocate limited testing-resource easily and efficiently and thus achieve the highest reliability objective during software module and integration testing.

Pragmatic study of parametric decomposition models for estimating software reliability growth

Numerous stochastic models for the software failure phenomenon based on Nonhomogeneous Poisson Process (NHPP) have been proposed in the last three decades (1968-98). Although these models are quite helpful for software developers and have been widely applied at industrial organizations or research centers, we still need to do more work on examining/estimating the parameters of existing software reliability growth models (SRGMs). We investigate and account for three possible trends of software fault detection phenomena during the testing phase: increasing, decreasing and steady state. We present empirical results from quantitative studies on evaluating the fault detection process and develop a valid time-variable fault detection rate model which has the inherent flexibility of capturing a wide range of possible fault detection trends. The applicability of the proposed model and the related methods of parametric decomposition are illustrated through several real data sets from different software projects. Our evaluation results show that the analytic parametric decomposition approach for SRGM have a fairly accurate prediction capability. In addition, the testing effort control problem based on the proposed model is also demonstrated.

Load-balanced anycast routing

For fault-tolerance and load-balance purposes, many modern Internet applications may require that a group of replicated servers dispersed widely over the world. The anycast as a new communication style defined in IPv6 provides the capability to route packets to the nearest server. Better quality of service (QoS) can be achieved by this kind of computing paradigm. DNS, Web service, and distributed database system are three most well known examples. However, before anycasting can be realized, more researches need to be done. The anycast routing scheme is one of the most important issues. In this paper, we propose a load-balanced anycast routing scheme based on the WRS (weighted random selection) method. We suggest that the server capability should be propagated along with other fields in the routing tables. An anycast routing algorithm should take into account the network transmission capability as well as the server processing capability for the selection of a target server. Three weight determination strategies are given. We also develop a simple algorithm to calculate the weights of WRS to achieve optimization under both the heavy and the light system traffic environment. Our approach is locally optimized to minimize the average total delay and well balanced for the server load.