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.

NHPP-based software reliability model considering testing effort and multivariate fault detection rate

In recent decades,many software reliability growth models（SRGMs） have been proposed for the engineers and testers in measuring the software reliability precisely.Most of them is established based on the non-homogeneous Poisson process（NHPP）,and it is proved that the prediction accuracy of such models could be improved by adding the describing of characterization of testing effort.However,some research work indicates that the fault detection rate（FDR） is another key factor affects final software quality.Most early NHPPbased models deal with the FDR as constant or piecewise function,which does not fit the different testing stages well.Thus,this paper first incorporates a multivariate function of FDR,which is bathtub-shaped,into the NHPP-based SRGMs considering testing effort in order to further improve performance.A new model framework is proposed,and a stepwise method is used to apply the framework with real data sets to find the optimal model.Experimental studies show that the obtained new model can provide better performance of fitting and prediction compared with other traditional SRGMs.

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.

Singularity of Some Software Reliability Models and Parameter Estimation Method

According to the principle, “The failure data is the basis of software reliability analysis”, we built a software reliability expert system (SRES) by adopting the artificial intelligence technology. By reasoning out the conclusion from the fitting results of failure data of a software project, the SRES can recommend users “the most suitable model” as a software reliability measurement model. We believe that the SRES can overcome the inconsistency in applications of software reliability models well. We report investigation results of singularity and parameter estimation methods of experimental models in SRES.

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.

Stochastic differential equation software reliability growth model with change-point

This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,considering the variation of failure distribution in the existing models during testing time is limited.These SDE SRGMs assume that failures have the same distribution.However,in practice,the fault detection rate can be affected by some factors and may be changed at certain point as time proceeds.With respect to this issue,in this paper,SDE SRGMs with changepoint are proposed to precisely reflect the variations of the failure distribution.A real data set is used to evaluate the new models.The experimental results show that the proposed models have a fairly accurate prediction capability.

A Bivariate Software Reliability Model with Change-Point and Its Applications

Testing-time when a change of a stochastic characteristic of the software failure-occurrence time or software failure-occurrence time-interval is observed is called change-point. It is said that effect of the change-point on the software reliability growth process influences on accuracy for software reliability assessment based on a software reliability growth model (SRGM). We propose an SRGM with the effect of the change-point based on a bivariate SRGM, in which the software reliability growth process is assumed to depend on the testing-time and testing-effort factors simultaneously, for accurate software reliability assessment. And we discuss an optimal software release problem for deriving optimal testing-effort expenditures based on our model. Further, we show numerical examples of software reliability assessment based on our bivariate SRGM and estimation of optimal testing-effort expenditures by using actual data.

A Censored Nonparametric Software Reliability Model

This paper analyses the effect of censoring on the estimation of failure rate, and presents a framework of a censored nonparametric software reliability model. The model is based on nonparametric testing of failure rate monotonically decreasing and weighted kernel failure rate estimation under the constraint of failure rate monotonically decreasing. Not only does the model have the advantages of little assumptions and weak constraints, but also the residual defects number of the software system can be estimated. The numerical experiment and real data analysis show that the model performs wdl with censored data.

Singularity of Software Reliability Models LVLM and LVQM

According to the principle, “The failure data is the basis of software reliabilityanalysis”, we built a software reliability expert system (SRES) by adopting the artificialtechnology. By reasoning out the conclusion from the fitting results of failure data of asoftware project, the SRES can recommend users “the most suitable model” as a softwarereliability measurement model. We believe that the SRES can overcome the inconsistency inapplications of software reliability models well. We report investigation results of singularity and parameter estimation methods of models, LVLM and LVQM.

Analysis on Some of Software Reliability Models

Software reliability and maintainability evaluation tool (SRMET 3.0) is introducted in detail in this paper, which was developed by Software Evaluation and Test Center of China Aerospace Mechanical Corporation. SRMET 3.0 is supported by seven software reliability models and four software maintainability models. Numerical characteristics for all those models are deeply studied in this paper, and corresponding numerical algorithms for each model are also given in the paper.

Quasi-Bayesian software reliability model with small samples

In traditional Bayesian software reliability models, it was assume that all probabilities are precise. In practical applications the parameters of the probability distributions are often under uncertainty due to strong dependence on subjective information of experts＇ judgments on sparse statistical data. In this paper, a quasi-Bayesian software reliability model using interval-valued probabilities to clearly quantify experts＇ prior beliefs on possible intervals of the parameters of the probability distributions is presented. The model integrates experts＇ judgments with statistical data to obtain more convincible assessments of software reliability with small samples. For some actual data sets, the presented model yields better predictions than the Jelinski-Moranda （JM） model using maximum likelihood （ML）.

Empirical Research on the Application of a Structure-Based Software Reliability Model

Reliability engineering implemented early in the development process has a significant impact on improving software quality.It can assist in the design of architecture and guide later testing,which is beyond the scope of traditional reliability analysis methods.Structural reliability models work for this,but most of them remain tested in only simulation case studies due to lack of actual data.Here we use software metrics for reliability modeling which are collected from source codes of post versions.Through the proposed strategy,redundant metric elements are filtered out and the rest are aggregated to represent the module reliability.We further propose a framework to automatically apply the module value and calculate overall reliability by introducing formal methods.The experimental results from an actual project show that reliability analysis at the design and development stage can be close to the validity of analysis at the test stage through reasonable application of metric data.The study also demonstrates that the proposed methods have good applicability.

A software reliability growth model for component-based software incorporating debugging delay and imperfect debugging

In view of the problems and the weaknesses of component-based software （ CBS ） reliability modeling and analysis, and a lack of consideration for real debugging circumstance of integration tes- ting, a CBS reliability process analysis model is proposed incorporating debugging time delay, im- perfect debugging and limited debugging resources. CBS integration testing is formulated as a multi- queue muhichannel and finite server queuing model （MMFSQM） to illustrate fault detection process （FDP） and fault correction process （FCP）. A unified FCP is sketched, given debugging delay, the diversities of faults processing and the limitations of debugging resources. Furthermore, the impacts of imperfect debugging on fault detection and correction are explicitly elaborated, and the expres- sions of the cumulative number of fault detected and corrected are illustrated. Finally, the results of numerical experiments verify the effectiveness and rationality of the proposed model. By comparison, the proposed model is superior to the other models. The proposed model is closer to real CBS testing process and facilitates software engineer＇ s quantitatively analyzing, measuring and predicting CBS reliability. K

A New Software Reliability Framework —— A Extended Cleanroom Model

Cleanroom software engineering has been proven effective in improving software development quality while at the same time increasing reliability. To adapt to large software system development, the paper presents an extended the Cleanroom model, which integrates object-oriented method based on stimulus history, reversed engineering idea, automatic testing and reliability assessment into software development. The paper discusses the architecture and realizing technology of ECM.

Lognormal Process Software Reliability Modeling with Testing-Effort

We propose a software reliability growth model with testing-effort based on a continuous-state space stochastic process, such as a lognormal process, and conduct its goodness-of-fit evaluation. We also discuss a parameter estimation method of our model. Then, we derive several software reliability assessment measures by the probability distribution of its solution process, and compare our model with existing continuous-state space software reliability growth models in terms of the mean square error and the Akaike’s information criterion by using actual fault count data.

A New Analysis Concept in Applying Software Reliability Growth Models and Tool Implementation: The SafeMan

In recent years, many software development organizations have been assessing and analyzing their software product’s reliability/quality and judging whether the software product is releasable by using Software Reliability Growth Models (SRGMs) at the final stage of software development. The usage of SRGMs originates in the advantage that various reliability analysis results based on the SRGMs can be acquired easily. However, it is very difficult for general software project managers to grasp the achievement level of reliability/quality based on its analysis results because some sort of professional knowledge is required in order to understand the information on the attainment progress of software product’s reliability/quality. Moreover, it is also difficult for software project managers and inspectors who do not deeply comprehend the details of their project to evaluate the degree of software reliability and quality, if they assess it without grasping the live development situation and only see the documents submitted from their staff. In this paper, we propose a new analysis concept for assessing the software product’s reliability/quality, and illustrate the output results obtained by a tool, the SafeMan.

Role of Software Reliability Models in Performance Improvement and Management

Software reliability models describe the failure behavior of the software. The models are used to evaluate the software quantitatively. They assess the reliability of the software by predicting faults or failures for a software. Reliability is one of important quality attributes of the software in which software end user is more interested rather than the software developer. Hence, the performance of a software can be improved by incorporating important quality attributes like reliability, maintainability and availability of the software along with performance attributes like response time and throughput. The paper discusses about the role played by important software reliability models in analyzing the failure prediction of the software. It also explores the strong relationship that exists between quality attributes and performance attributes. With some illustrations highlighting the necessity of in-depth understanding of the link that exists between reliability and performance of the software. The derived knowledge helps in improving the performance of the software sustainably over a period of time and manage the software more effectively.

Improved Genetic Programming Algorithm Applied to Symbolic Regression and Software Reliability Modeling

The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first inves-tigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operation. The IGP is examined in two domains and the results suggest that the IGP is more effective and more efficient than the canonical one applied in different domains.

A Bootstrapping Approach for Software Reliability Measurement Based on a Discretized NHPP Model

Discrete software reliability measurement has a proper characteristic for describing a software reliability growth process which depends on a unit of the software fault-detection period, such as the number of test runs, the number of executed test cases. This paper discusses discrete software reliability measurement based on a discretized nonhomogeneous Poisson process (NHPP) model. Especially, we use a bootstrapping method in our discrete software reliability measurement for discussing the statistical inference on parameters and software reliability assessment measures of our model. Finally we show numerical examples of interval estimations based on our bootstrapping method for the several software reliability assessment measures by using actual data.

A New Software Reliability Growth Model: Genetic-Programming-Based Approach

A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is;the nature of each project makes it difficult to build a model which can generalize. In this paper we propose the use of Genetic Programming (GP) as an eVolutionary computation approach to handle the software reliability modeling problem. GP deals with one of the key issues in computer science which is called automatic programming. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve problems. GP will be used to build a SRGM which can predict accumulated faults during the software testing process. We evaluate the GP developed model and compare its performance with other common growth models from the literature. Our experiments results show that the proposed GP model is superior compared to Yamada S-Shaped, Generalized Poisson, NHPP and Schneidewind reliability models.