Discrete Software Reliability Growth Modeling for Errors of Different Severity Incorporating Change-point Concept

Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.

Incorporating S-shaped testing-effort functions into NHPP software reliability model with imperfect debugging

Testing-effort（TE） and imperfect debugging（ID） in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models（SRGMs）. For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions（TEFs）, i.e.,delayed S-shaped TEF（DS-TEF） and inflected S-shaped TEF（IS-TEF）, are proposed. Then these two TEFs are incorporated into various types（exponential-type, delayed S-shaped and inflected S-shaped） of non-homogeneous Poisson process（NHPP）SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new SRGMs.The experimental results show that：（i） the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs;（ii） incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs;（iii） the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.

Web software reliability modeling with random impulsive shocks

As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large num- ber of software reliability growth models （SRGMs）, including those combined with multiple change-points （CPs）, have been available, these conventional SRGMs cannot be directly applied to web soft- ware reliability analysis because of the complex web operational profile. To characterize the web operational profile precisely, it should be realized that the workload of a web server is normally non-homogeneous and often observed with the pattern of random impulsive shocks. A web software reliability model with random im- pulsive shocks and its statistical analysis method are developed. In the proposed model, the web server workload is characterized by a geometric Brownian motion process. Based on a real data set from IIS server logs of ICRMS website （www.icrms.cn）, the proposed model is demonstrated to be powerful for estimating impulsive shocks and web software reliability.

Component-based software reliability process simulation considering imperfect debugging

In view of the flaws of component-based software （CBS） reliability modeling and analysis, the low recognition degree of debugging process, too many assumptions and difficulties in obtaining the solution, a CBS reliability simulation process is presented incorporating the imperfect debugging and the limitation of debugging resources. Considering the effect of imperfect debugging on fault detec- tion and correction process, a CBS integration testing model is sketched by multi-queue muhichannel and finite server queuing model （MMFSQM）. Compared with the analytical method based on pa- rameters and other nonparametric approaches, the simulation approach can relax more of the usual reliability modeling assumptions and effectively expound integration testing process of CBS. Then, CBS reliability process simulation procedure is developed accordingly. The proposed simulation ap- proach is validated to be sound and effective by simulation experiment studies and analysis.

Reliability Growth Modeling and Optimal Release Policy Under Fuzzy Environment of an N-version Programming System Incorporating the Effect of Fault Removal Efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery （recovery block scheme）, fault masking （N-version programming （NVP）） or a combination of both （Hybrid scheme）. These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model （SRGM） is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Early Stage Software Reliability Estimation with Stochastic Reward Nets

This paper presents software reliability modeling issues at the early stage of a software development for fault tolerant software management system. Based on Stochastic Reward Nets, an effective model of hierarchical view for a fault tolerant software management system is put forward, and an approach that consists of system transient performance analysis is adopted. A quantitative approach for software reliability analysis is given. The results show its usefulness for the design and evaluation of the early-stage software reliability modeling when failure data is not available.

A software cost model with maintenance and risk costs for safety-critical systems

According to the consequences of software failures, software faults remaining in safety-critical systems can be classified into two sets： common faults and fatal faults. Common faults cause slight loss when they are activated. A fatal fault can lead to significant loss, and even damage the safety-crltical system entirely when it is activated. A software reliability growth model for safety-critical systems is developed based on G - 0 model. And a software cost model is proposed too. The cost model considers maintenance and risk costs due to software failures. The optimal release policies are discussed to minimize the total software cost. A numerical exampie is provided to illustrate how to use the results we obtained.

A Study of Quantitative Progress Evaluation Models for Open Source Projects

Open source software (OSS) has become an indispensable part of society, not only for personal use but also for corporate use. Projects developed and operated by OSS are called open source projects, and the number of such projects is increasing. On the other hand, because anyone can participate in an open source project, the progress of the project is uncertain due to differences in project members’ skills, development environments, and time zones of activity. Therefore, many users and companies need to understand the development and operation status of open source project. Then, the developers carefully make decisions on upgrading or installing new OSS. In this paper, we focus on the maintenance effort estimation for open source projects considering uncertainty. Also, we evaluate the project quantitatively using Earned Value Management (EVM). Moreover, we examine the appropriateness of the model for predicting the maintenance effort expeditures. Furthermore, we discuss the appropriateness of this EVM method.

An Improved NHPP Model with Time-Varying Fault Removal Delay

In this paper, an improved NHPP model is proposed by replacing constant fault removal time with time-varying fault removal delay in NHPP model, proposed by Daniel R Jeske. In our model, a time-dependent delay function is established to fit the fault removal process. By using two sets of practical data, the descriptive and predictive abilities of the improved NHPP model are compared with those of the NHPP model, G-O model, and delayed S-shape model. The results show that the improved model can fit and predict the data better.