Software Reliability Growth Process Research Articles

Software Shipping Time Estimation for Markovian Change-Point Reliability Model

A Markovian software reliability model is known as one of the useful models which enable us to describe the details on the dynamic behavior of software reliability growth process with imperfect debugging activities. As the application of this model for software development management, we discuss software shipping time estimation based on the extended Markovian software reliability model considering with the effect of change-point, which is the testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably. Especially in our discussion on the software shipping time estimation, we derive an analytical software shipping policy under certain software reliability objective and show numerical examples of the application of the software shipping policy by using actual software reliability data. Finally, we give some useful consideration on the relationship between the software shipping time and the software reliability growth process for conducing quality-oriented software development management.

Markovian Software Reliability Modeling with Change-Point

We discuss a Markovian modeling approach for software reliability assessment with the effects of change-point and imperfect debugging environment. Testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably is called change-point. Taking into account the effect at change-point in software reliability growth modeling is important to improve the accuracy of software reliability assessment. Our modeling approach describes a software reliability growth process with not only the effect of change-point but also the imperfect debugging activities based on a semi-Markov process for reflecting actual situation of debugging activities. Finally, we show numerical examples of our model for software reliability analysis and check the performance of our model with an existing Markovian software reliability growth model by using actual data.

On Estimation of Number of Detectable Software Faults under Budget Constraint

Checking software quality and reliability of developed software system is one of the important activities for developing highly-reliable software system. In software testing phase, which is located in the final stage of software development process, software faults remaining the developed software system must be eliminated as possible as the testing manager could. Further, it is also important to develop testing plan to conduct testing activities efficiently under the certain constraints because there are some constraints, such as cost and delivery, in the actual testing phase. This paper discusses method to estimate the number of detectable faults under constant budget constraint by applying a two-dimensional Weibull type software reliability model, in which the software reliability growth process depends on testing time and testing effort factors. We finally show numerical examples of our method by using actual data.

Test Environment-Dependent Discrete Binomial-Type Software Reliability Models

We have no doubt that the software reliability growth process in the testing phase depends on the test environment factors, such as the attained testing coverage, the number of test-runs and the debugging skills, which affect the software failure occurrence or fault detection phenomenon in the testing phase. In this paper, we propose software reliability models that consider the effects of the testing environment factors. Our models are developed by a program size-dependent discrete binomial-type software reliability modeling approach. This modeling approach is also consistent with software reliability data collection and enables us to consider the effect of the program size. Finally, we compare the accuracy of our models in terms of mean square errors (MSE) and Akaike’s information criterion (AIC) with the existing corresponding model by using actual data.

Finding the best bug fixing rate and bug fixing time using software reliability modelling

This article is mainly focused on finding the best possible way to rectify bug fixing rate (BFR) and bug fixing time (BFT). Further, versatile software projects have been verified when materialising the bug fixing rate. To increase the bug fixing rate, bug traceability is reduced by virtue of version tag in each and every component of a software deliverable. Software build release time is optimised by using mathematical optimisation techniques such as software reliability growth and non-homogeneous Poisson process models. This is very much essential in the present market scenario. The build inconsistency and automation are also rectified in this erudite research work. The developed software is free from defects and improves the software quality by increasing bug fixing rate.

Finding the best bug fixing rate and bug fixing time using software reliability modelling

This article is mainly focused on finding the best possible way to rectify bug fixing rate (BFR) and bug fixing time (BFT). Further, versatile software projects have been verified when materialising the bug fixing rate. To increase the bug fixing rate, bug traceability is reduced by virtue of version tag in each and every component of a software deliverable. Software build release time is optimised by using mathematical optimisation techniques such as software reliability growth and non-homogeneous Poisson process models. This is very much essential in the present market scenario. The build inconsistency and automation are also rectified in this erudite research work. The developed software is free from defects and improves the software quality by increasing bug fixing rate.

Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor

We observe a phenomenon that the probabilistic characteristic of software failure-occurrence time-interval change notably in an actual testing-phase of a software development process. Testing-time observing such phenomenon is ordinarily called change-point. This phenomenon is treated as one of the factors affecting the accuracy of software reliability assessment based on a software reliability growth model. And regarding software reliability growth modeling, a bivariate software reliability growth model, which describes a software reliability growth process depending on the software reliability growth factor consists of testing-time and testing-effort factors, has been proposed for improving software reliability assessment accuracy based on the software reliability growth process. In this paper, we develop a bivariate software reliability growth model considering with the uncertainty of the change of software failure-occurrence phenomenon at the change-point for improving more the accuracy. Finally we show numerical examples of our model by using actual data.

OPTIMAL SOFTWARE SHIPPING TIME ESTIMATION BASED ON A CHANGE-POINT HAZARD RATE MODEL

Software failure-occurrence or the fault-detection phenomenon is changed notably in an actual testing phase or an operational phase due to the changes of factors influencing the software reliability growth process. Taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment because it is said that such approach enables us to conduct more plausible software reliability assessment reflecting an actual testing-environment. We develop a framework for developing software hazard rate models with effect of change-point, and discuss change-point detection methods for applying our model to quantitative software reliability assessment. Additionally, we discuss an optimal software release problem for estimating optimal shipping time with the effect of the change as one of the application problems of our model in software project management. Finally, we show numerical examples for software reliability assessment and our optimal software release policy based on our change-point model by using actual data.

Review on Software Reliability Growth Models and Software Release Planning

paper, Software Reliability Engineering is a field that developed from ancestry in the reliability disciplines of structural, electrical, and hardware engineering. Reliability models are powerful tools of Software Reliability Engineering for estimating, predicting, devious, and assessing software reliability. On the basis of the review the cataloging of software reliability models has been presented as a major part. This categorization is based on the various dimensions of reliability models. Models under review reflect either infinite or finite number of failures. This paper discusses a two- dimensional software reliability growth modeling framework. We measured that an actual software reliability growth progression depends not only on testing time but also on testing effort and also enables us to portray software release planning problem in software reliability growth process. Thus, we can say that software project managers can demeanor more viable and accurate software reliability appraisal by using two-dimensional SRGM.

EXTENDED HAZARD RATE MODELS FOR SOFTWARE RELIABILITY ASSESSMENT WITH EFFECT AT CHANGE-POINT

A software hazard rate model is known as one of the important and useful mathematical models for describing the software failure occurrence phenomenon observed in a testing phase. It is difficult to say that the testing environment always constant during a testing phase due to changing the specification and fault target and so forth. Therefore, taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment. In this paper, we develop extended software hazard rate models based on well-known Jelinski–Moranda and Moranda models, by considering with a change of testing environment. Especially in this paper, we incorporate the uncertainty of the effect of the change on the software reliability growth process into the software hazard rate modeling. Finally, we show numerical examples for our models and results of model comparisons by using actual data.

Optimal release policies for a software system with warranty cost and change-point phenomenon

Determining the software quality and release time of the software is an important role of software reliability. Software reliability growth models (SRGMs) are applications of software reliability. The change-point is defined as the prominent change in the software testing time, in connection with the quality of a software error occurrence or error-detection phenomenon. As a result, the software reliability growth process, based on the change-point, affects the precision of the software reliability prediction, based on the SRGMs. To find the accurate total expected delivery cost with a suitable warranty period of the software system, a new cost model for the software system with the warranty and change-point phenomenon is proposed in this paper. The entire expected delivery cost and the reliability of the software system is calculated using the change-point SRGM. The optimal release time is calculated by reducing the overall estimated delivery cost for various desired reliability levels. Based on the proposed warranty cost model and the reliability of the software system, we have derived some optimal release policies. Numerical illustrations and interconnected discussed data are itemized. From the testing outcome, we get software release policies that provide a comprehensive analysis of software based on the total expected delivery cost with a suitable warranty period, desired level of reliability, and change-point. Moreover, these policies will be helpful for project managers to decide where to stop the testing for customer release at the exact time with a suitable warranty period.

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.

CODESIGN-ORIENTED AND USER-PERCEIVED SERVICE AVAILABILITY MEASUREMENT FOR HARDWARE/SOFTWARE SYSTEM

Service availability is one of the customer-oriented attribute and defined as the attribute that the system can successfully satisfy the customers' requests. This paper discusses the stochastic service availability measurement for the computer-based system incorporating the concept of hardware/software codesign. We assume that the computer-based system consists of one hardware subsystem and one software subsystem and consider the situation where one customer intermittently uses the system which is operating and available anytime. From the viewpoint of a customer, occurrence of a system failure is recognized when either one of the following two events arises: A system failure occurs when the customer is using the system, or a usage request occurs when the system is down. We propose and derive three kinds of novel service-oriented system availability assessment measure. These are given as the functions of time and the number of software debuggings. The time-dependent behaviors of the system alternating between up and down states and the customer's request are described by a Markov process. Especially for the software subsystem, we incorporate the dynamic software reliability growth process, the upward tendency of difficulty in debugging, and the imperfect debugging environment into the model. Finally, we present several numerical examples of these measures for system service availability analysis.

STOCHASTIC SOFTWARE PERFORMABILITY EVALUATION BASED ON NHPP RELIABILITY GROWTH MODEL

In this paper, we discuss the software performability evaluation considering the real-time property; this is defined as the attribute that the system can complete the task within the stipulated response time limit. The dynamic software reliability growth process is described by the nonhomogeneous Poisson process (NHPP). Assuming that the software system can process multiple tasks simultaneously and that the arrival process of the tasks also follows an NHPP, we analyze the distribution of the number of tasks whose processes can be completed within the processing time limit with the infinite-server queuing model. We derive several software performability measures considering the real-time property. Finally, we illustrate several numerical examples of the measures and discuss the software performability analysis.

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.

TWO-DIMENSIONAL CHANGE-POINT MODELING FOR SOFTWARE RELIABILITY ASSESSMENT

Most of software reliability growth models (SRGMs) describe a software reliability growth process depending on only testing-time. However, it is said that a software reliability growth process in an actual testing-phase of a software development process depends on not only testing-time but also testing-effort factors. And we often observe a phenomenon that stochastic characteristics of the software failure-occurrence time or the software failure-occurrence time-interval changes notably in an actual testing-phase. The testing-time when such phenomenon is observed is called change-point. It is said that the effect of change-point on the software reliability growth process influences accuracy for software reliability assessment based on conventional SRGMs. This paper discusses a two-dimensional software reliability growth modeling with change-point for describing an actual phenomenon being related to the software reliability growth process. Further, we show examples of the applications of software reliability assessment based on our two-dimensional SRGM by using actual data.

STOCHASTIC DIFFERENTIAL EQUATION BASED SRGM FOR ERRORS OF DIFFERENT SEVERITY WITH TESTING-EFFORT

Models that describe the failure phenomenon and consequent enhancement in reliability due to fault removal are termed as Software Reliability Growth Models (SRGM). As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous-state space. Several continuous-state space SRGM based on stochastic differential equations of [Formula: see text] type to assess software reliability for large scale software systems have been proposed in the literature so far. However these continuous-state space SRGM have seldom taken the effect of testing-effort into consideration. The resources, such as manpower used for fault detection/removal, number of executed test-cases and CPU hours spent in executing software under test, are well-known as one of the most important factors related to the software reliability growth process. Some of the existing SRGM define errors of different severity. Severity of a failure or fault is the impact it has on the operation of a software-based system. Different faults may require different amount of testing efforts and testing strategy for their removal from the software. The aim of this paper is to determine the type of faults and their proportion present in the software. We have also assumed that learning of removal team grows as testing progresses due to experience and have incorporated the logistic removal rate during modeling of different types of faults with testing-effort by applying a mathematical technique of stochastic differential equations of [Formula: see text] type. Finally, a goodness-of-fit comparison between proposed models and existing continuous-state space SRGM using stochastic differential equations has been conducted.

Performability Modeling for Software System with Performance Degradation and Reliability Growth

In this paper, we discuss software performability evaluation considering the real-time property; this is defined as the attribute that the system can complete the task within the stipulated response time limit. We assume that the software system has two operational states from the viewpoint of the end users: one is operating with the desirable performance level according to specification and the other is with degraded performance level. The dynamic software reliability growth process with performance degradation is described by the extended Markovian software reliability model with imperfect debugging. Assuming that the software system can process the multiple tasks simultaneously and that the arrival process of the tasks follows a nonhomogeneous Poisson process, we analyze the distribution of the number of tasks whose processes can be completed within the processing time limit with the infinite server queueing model. We derive several software performability measures considering the real-time property; these are given as the functions of time and the number of debugging activities. Finally, we illustrate several numerical examples of the measures to investigate the impact of consideration of the performance degradation on the system performability evaluation.

PERFORMANCE ANALYSIS BASED ON THE NUMBER OF DEBUGGINGS FOR SOFTWARE SYSTEM WITH PROCESSING TIME LIMIT USING RELIABILITY GROWTH MODEL

We propose the performance evaluation method for the multi-task system with software reliability growth process. The software fault-detection phenomenon in the dynamic environment is described by the Markovian software reliability model with imperfect debugging. We assume that the cumulative number of tasks arriving at the system follows the homogeneous Poisson process. Then we can formulate the distribution of the number of tasks whose processes can be complete within a prespecified processing time limit with the infinite-server queueing model. From the model, several quantities for software performance measurement considering the real-time property can be derived and given as the functions of time and the number of debuggings. Finally, we present several numerical examples of the quantities to analyze the relationship between the software reliability characteristics and the system performance measurement.

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