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.

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.

SOFTWARE RELIABILITY MODEL FOR COMPONENT INTERACTION MODE

With the rapid progress of component technology,the software development methodology of gathering a large number of components for designing complex software systems has matured.But,how to assess the application reliability accurately with the information of system architecture and the components reliabilities together has become a knotty problem.In this paper,the defects in formal description of software architecture and the limitations in existed model assumptions are both analyzed.Moreover,a new software reliability model called Component Interaction Mode(CIM) is proposed.With this model,the problem for existed component-based software reliability analysis models that cannot deal with the cases of component interaction with non-failure independent and non-random control transition is resolved.At last,the practice examples are presented to illustrate the effectiveness of this model.

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.

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.

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）.

Physics of Failure(PoF)-Based Reliability and Performance Integrating Modeling Method and Software for Electromechanical Product

Electromechanical product's reliability is affected by uncertainty as well as performance degeneration during its life cycle.The present reliability and performance integrating modeling methods have obvious deficiencies in long period reliability analysis and assessment when applied to such system.A novel integrating modeling method based on physics of failure(PoF)and a simulation algorithm that considers uncertainty and degeneration are proposed in this paper to compute maintenance free operation period or maintenance free operation period survivability which is used to assess long period reliability of system.Furthermore,the concept design of this kind of software based on the above theory is also introduced.A case study of servo valve demonstrates the feasibility of the method and usability of the software in this research.

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.

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.

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.

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.

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.

Component-Oriented Reliability Analysis Based on Hierarchical Bayesian Model for an Open Source Software

The successful experience of adopting distributed development models in such open source projects includes GNU/Linux operating system, Apache HTTP server, Android, BusyBox, and so on. The open source project contains special features so-called software composition by which several geographically-dispersed compo-nents are developed in all parts of the world. We propose a method of component-oriented reliability as-sessment based on hierarchical Bayesian model and Markov chain Monte Carlo methods. Especially, we fo-cus on the fault-detection rate for each component reported to the bug tracking system. We can assess the reliability for the whole open source software system by using the confidence interval for each component. Also, we analyze actual software fault-count data to show numerical examples of reliability assessment for OSS.

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.

Using Artificial Neural-Networks in Stochastic Differential Equations Based Software Reliability Growth Modeling

Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and measuring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the testing and operational phase as a counting process. Moreover, if the size of software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore in such a situation, we can model the software fault detection process as a stochastic process with a continuous state space. Recently, Artificial Neural Networks (ANN) have been applied in software reliability growth prediction. In this paper, we propose an ANN based software reliability growth model based on Ito type of stochastic differential equation. The model has been validated, evaluated and compared with other existing NHPP model by applying it on actual failure/fault removal data sets cited from real software development projects. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP based model.

Classification and Appraisement for Nonparametric Software Reliability Models

A taxonomy of software reliability models is developed that the models are classified as parametric and nonparametric models, and the nonparametric models are classified according to the mathematical methods they used. Then, a practical appraising index system for nonparametric software reliability models are put forward. The nonparametric software reliability models are classified into 5 classes, that is time series analysis models, grey theo- ry forecasting models, artificial neural network models, wavelet analysis models and kernel estimation models, and they are evaluated by the practical index system.

A Software Reliability Model for OSS Including Various Fault Data Based on Proportional Hazard-Rate Model

The software reliability model is the stochastic model to measure the software reliability quantitatively. A Hazard-Rate Model is the well-known one as the typical software reliability model. We propose Hazard-Rate Models Considering Fault Severity Levels (CFSL) for Open Source Software (OSS). The purpose of this research is to make the Hazard-Rate Model considering CFSL adapt to baseline hazard function and 2 kinds of faults data in Bug Tracking System (BTS), i.e., we use the covariate vectors in Cox proportional Hazard-Rate Model. Also, we show the numerical examples by evaluating the performance of our proposed model. As the result, we compare the performance of our model with the Hazard-Rate Model CFSL.