Discrete Software Reliability Growth Model Research Articles

Discrete Software Reliability Growth Models with Discrete Test Effort Functions

Discrete Software Reliability Growth Models with Discrete Test Effort Functions

A Discrete SRGM for Multi-Release Software System with Faults of Different Severity

To meet highly competitive market challenges along with technological upgrades and changing user requirements, software firms offer newer versions of their products by adding new features and new functionalities. But this also results in increase in the fault content of software. In this paper we propose a discrete software reliability growth model (SRGM) for fault removal process incorporating the effect of up-gradations on the subsequent releases of the software. It is further assumed that faults present in the software are not of the same type and may be classified as simple and hard faults depending upon the effort and time consumed for their removal. The proposed model has been validated on a real life dataset for software with four releases. The results obtained are encouraging and fairly accurate.

An NHPP SRGM with Change Point and Multiple Releases

In an environment of intense competition, software upgrades have become the necessity for the survival in software industry. In this paper, the authors propose a discrete Software Reliability Growth Model (SRGM) for the software with successive releases by taking into consideration the realistic assumption that Fault Removal Rate (FRR) may not remain constant during the testing process, it changes due to severity of faults detected and due to change in strategies adapted by testing team and the time point where FRR changes is called the Change Point. Many researchers have developed SRGMs incorporating the concept of Change Point for single release software. The proposed model aims to present multi release software reliability modeling with change point. Discrete logistic distribution function has been used to model relationship between features enhancement and fault removal. It is helpul in developing a flexible SRGM, which is S-shaped in nature. In order to evaluate the proposed SRGM, parameter estimation is done using the real life data set for software with four releases and the goodness-of-fit of this model is analyzed.

A discrete SRGM for multi release software system

Due to demand of highly reliable software system and intense competition of the market the software developers are forced to come up with new features and enhancements in the software at a very frequent rate. Though it adds a new leash of life to the product, but it also leads to complexity and bugs in the software system. The phenomenon of adding features and functionalities to the existing products is called up-gradation. Most of the work done in software reliability engineering pertains to single release software system. These models do not take into consideration the effect of up-gradations on the subsequent releases. In this paper, we propose a discrete software reliability growth model for fault removal process of multi release software products. The proposed model is validated on real failure dataset for software with four releases. The results obtained are encouraging and fairly accurate.

Discrete Software Reliability Growth Models with Discrete Test Effort Functions

Number of software Reliability growth models has been proposed in the literature. A mathematical technique which describes the software testing phenomenon known as the software reliability growth model. Software reliability growth models are used to predict the number of faults and reliability of the software. In the view of this software reliability growth models are basically differentiated as the continuous and discrete models. There is a plenty of development in the continuous models but little towards the discrete models. In this paper we have presented a discrete reliability growth model with different discrete testing effort functions and the same time software release policy is discussed. A new imperfect debugging discrete software reliability growth model with testing effort is proposed. All calculations are done on real data. The results shows the proposed testing effort models are perfectly fit to the data.

Discrete Software Reliability Growth Models with Discrete Test Effort Functions

Number of software Reliability growth models has been proposed in the literature. A mathematical technique which describes the software testing phenomenon known as the software reliability growth model. Software reliability growth models are used to predict the number of faults and reliability of the software. In the view of this software reliability growth models are basically differentiated as the continuous and discrete models. There is a plenty of development in the continuous models but little towards the discrete models. In this paper we have presented a discrete reliability growth model with different discrete testing effort functions and the same time software release policy is discussed. A new imperfect debugging discrete software reliability growth model with testing effort is proposed. All calculations are done on real data. The results shows the proposed testing effort models are perfectly fit to the data.

MAXIMUM ENTROPY AND DISCRETE TIME SOFTWARE RELIABILITY GROWTH MODELS

This paper provides a preliminary investigation into the application of the Maximum Entropy Principle (MEP), introduced by Jaynes in 1957, in modeling discrete time Software Reliability Growth Model (SRGM). On their own, each of these two topics are interesting with extensive applications, and here we will show how they can be combined to provide yet another application of the MEP among a huge array of proven successful applications. A brief discussion of MEP and SRGM will be given and a hitherto unnoticed relationship between MEP distribution and the Lagrange interpolation polynomials highlighted. We then show how MEP can be used to obtain some important distributions arising from discrete time SRGM. Finally, a simple example is given to illustrate the theory.

ON THE DEVELOPMENT OF UNIFIED SCHEME FOR DISCRETE SOFTWARE RELIABILITY GROWTH MODELING

The importance of Software Reliability Growth Models to control the testing process and for quantitative assessment of software reliability is a well established fact. However, difficulties created by their underlying assumptions, their relevance and validity to real testing environment have made the selection of appropriate model an uphill task. Recently, new dimensions have been added to software reliability engineering with the development of unified modeling schemes. These schemes have proved seminal in the development of the general theory, partially because of their simplicity and mathematical tractability. In this paper, we propose a unified scheme for discrete software reliability growth modeling using the fault detection/correction rate function. Standard probability distributions have been used to model the fault correction and detection times. Initially, we have formulated the unified scheme when the fault correction is immediate to the failure observation and later we extend it to the cases where removal is a two stage process namely failure observation followed by fault detection/correction. The use of fault detection/correction rate or else known as hazard rate to represent stage wise removal of fault during testing highlights the importance of the proposed framework. Convolution of probability distribution functions has been used to represent Stage-wise removal of fault i.e. failure observation, fault detection/fault correction. Few Discrete models have been derived using the proposed methodology. Parameter estimation on two real software failure datasets has been worked out. The results obtained are fairly accurate and quite encouraging.

Integrable difference equations for software reliability assessment and their applications

This paper discusses software reliability growth models (SRGMs) based on integrable difference equations, which are derived from the original nonhomogeneous Poisson process models by using Hirota’s bilinearization methods. After that, goodness-of-fit comparisons of our discrete models with existing deterministic discrete models are performed by using actual data sets. Further, we disucss discrete optimal software release problems under simultaneous cost and reliability requirements based on our discrete SRGMs.

Discrete Program-Size Dependent Software Reliability Assessment: Modeling, Estimation, and Goodness-of-Fit Comparisons

In this paper we propose a discrete program-size dependent software reliability growth model flexibly describing the software failure-occurrence phenomenon based on a discrete Weibull distribution. We also conduct model comparisons of our discrete SRGM with existing discrete SRGMs by using actual data sets. The program size is one of the important metrics of software complexity. It is known that flexible discrete software reliability growth modeling is difficult due to the mathematical manipulation under a conventional modeling-framework in which the time-dependent behavior of the cumulative number of detected faults is formulated by a difference equation. Our discrete SRGM is developed under an existing unified modeling-framework based on the concept of general order-statistics, and can incorporate the effect of the program size into software reliability assessment. Further, we discuss the method of parameter estimation, and derive software reliability assessment measures of our discrete SRGM. Finally, we show numerical examples of discrete software reliability analysis based on our discrete SRGM by using actual data.

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.

Generalized Discrete Software Reliability Modeling With Effect of Program Size

Generalized methods for software reliability growth modeling have been proposed so far. But, most of them are on continuous-time software reliability growth modeling. Many discrete software reliability growth models (SRGM) have been proposed to describe a software reliability growth process depending on discrete testing time such as the number of days (or weeks); the number of executed test cases. In this paper, we discuss generalized discrete software reliability growth modeling in which the software failure-occurrence times follow a discrete probability distribution. Our generalized discrete SRGMs enable us to assess software reliability in consideration of the effect of the program size, which is one of the influential factors related to the software reliability growth process. Specifically, we develop discrete SRGMs in which the software failure-occurrence times follow geometric and discrete Rayleigh distributions, respectively. Moreover, we derive software reliability assessment measures based on a unified framework for discrete software reliability growth modeling. Additionally, we also discuss optimal software release problems based on our generalized discrete software reliability growth modeling. Finally, we show numerical examples of software reliability assessment by using actual fault-counting data

On Discrete Software Reliability Growth Model & Categorization of Faults

Since the introduction of the concept of Software Reliability, several Software Reliability Growth Models (SRGM) have been developed to assess the growth of reliability during the testing phase. But little seems to have been done to categorize the faults on the basis of their severity and complexity. In this paper we first develop a discrete time SRGM to describe and predict the error removal phenomenon then use it to develop fault categories followed by size of each category. Three different forms of the discrete model are given and their equivalence is proved. It is also shown how the continuous time models can de derived.

A General Discrete Software Reliability Growth Model

The authors develop a general discrete software reliability growth model based on nonhomogenous Poisson process. The software faults are categorized according to severity; corresponding effort is needed to detect and remove them. In other words, there may be delay in their detection and subsequent removal. The authors show that the model is flexible enough and fits well with a variety of actual data sets obtained from different software projects. Predictivity of the model is checked in terms of Akaike information criterion and sum of squared errors. The proposed model is also checked against different components of the model, including other existing ones, thus highlighting its applicability.

A discrete software reliability growth model with two types of errors

AbstractAs the social missions of computer systems increase, it becomes more important to develop high‐reliability software. For this reason software systems are tested repeatedly to remove latent errors in the testing phase during software development. One of the most interesting methods is to evaluate software reliability by using test data observed in the error‐detection process. In this paper we model the error‐detection process as a stochastic model and introduce several effective measures to evaluate software reliability. In modeling we use the number of test run trials as a unit of the error‐detection period and assume that the cumulative number of software errors detected in an arbitrary testing time interval is a nonhomogeneous Poisson process. Further, we consider the difficulty of error detection because the reliability of software is evaluated from the characteristics and frequency of the errors. Applying the model to the actual test data, we perform the goodness‐of‐fit test and infer the software reliability measures. Finally, we discuss an optimum software release problem based on the software reliability index.

Discrete software reliability growth models

AbstractA general description of a discrete software reliability growth model, which adopts the number of test runs or the number of executed test cases as the unit of error detection period, is presented. Two classes of discrete software reliability growth models are proposed and discussed. These models can be described by non‐homogeneous Poisson processes, in which the random variable is denned as the number of errors detected by n test runs (n = 0, 1, 2,…). The application and comparison of these models to actual software error data are shown.