Existing Software Reliability Growth Models Research Articles

Chen Software Reliability Growth Model

Software reliability analysis is very vital in software development. Software manufacturers assess the quality of their developed software through this analysis. This has triggered the development of reliability models. Software reliability growth models have been used extensively to examine the quality of manufactured software before they are sent to the market. This study presents a new software reliability growth model using Chen distribution. The Chen software reliability growth model is then used to establish sequential probability ratio test limits for determining whether a manufactured software is reliable or unreliable. The applications of the proposed model revealed that it performs better than some of the existing software reliability growth models for the given datasets.

Bivariate Software Reliability Growth Models under Budget Constraint for Development Management

Software reliability growth is observed by investing not only the testing-time but also the testing-effort in the testing-phase of software development process. If the testing-time (testing-effort) is reduced to some extent, it is possible to observe the software reliability growth by investing the amount of testing-effort (testing-time) which can compensate the insufficiency of the testing-time (testing-effort). However, most of the existing software reliability growth models (SRGMs) are constructed as univariate models and the substitutability between the testing-time and testing-effort is not considered. Additionally, it is necessary to remove many faults efficiently within the budget. In this paper, we develop bivariate Weibull type SRGMs under budget constraint based on the Cobb-Douglas type and CES (constant elasticity of substitution) type testing-time functions. Simultaneously, we evaluate the substitutability between the testing-time and testing-effort factors which are software reliability growth factors. Finally, we conduct the sensitivity analysis and show numerical examples by using actual data sets.

Two-Dimensional NHPP Models Based on Several Testing-Time Functions and Their Applications

Enormous testing-time and testing-effort (central processing unit (CPU) time, execution time, man-hour, test coverage, executed test case, and so forth) are invested in testing-phase for enhancing software reliability. Although they are software reliability growth factors, existing software reliability growth models (SRGMs) are not introduced simultaneously. Also, the software reliability growth factors enable to substitute each other partly. Therefore, we develop new bivariate nonhomogeneous Poisson process (NHPP) models based on two types of testing-time functions in this paper. Concretely, we assume that the testing-time as a software reliability growth factor is composed of the testing-time and testing-effort factors. Then, the testing-time as the software reliability growth factor is expressed by the Cobb–Douglas and constant elasticity of substitution (CES) type testing-time functions. We conduct goodness-of-fit comparisons of existing models with proposed bivariate models. Also, we discuss estimation method of the optimal software release time and optimal amount of testing-effort as an application. Finally, we show numerical examples by using actual datasets.

A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal

Most existing software reliability growth models (SGRMs) often assume software faults are mutually independent and the detected faults can be perfectly removed. However, those two assumptions are not realistic in practice since the dependent faults can also exist in the program. At the same time, it is unlikely to correct all the detected faults in the testing phase due to the limitation of testing resource, the skill and experience of the programmer, and multi-release consideration for software organization. This paper presents a new non-homogeneous Poisson process (NHPP) software reliability model with a pioneering idea by considering software fault dependency and imperfect fault removal. In order to clearly explain software fault dependency, some facts and examples are discussed in Section 1. Two types of software faults are defined, Type I (independent) fault and Type II (dependent) fault, according to fault dependency. Two phases debugging processes, Phase I and Phase II, are proposed according to the debugged software fault type. A small portion of software faults that software testers are not able to remove is also considered in both phases in the proposed model. The illustration of the model effectiveness is based on the three datasets collected from industries. Some limitations of the proposed model are also discussed in the last section.

A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.

A software reliability model with time-dependent fault detection and fault removal

The common assumption for most existingsoftware reliability growth models is that fault is independent and can be removed perfectly upon detection. However, it is often not true due to various factors including software complexity, programmer proficiency, organization hierarchy, etc. In this paper, we develop a software reliability model with considerations of fault-dependent detection, imperfect fault removal and the maximum number of faults software. The genetic algorithm (GA) method is applied to estimate the model parameters. Four goodness-of-fit criteria, such as mean-squared error, predictive-ratio risk, predictive power, and Akaike information criterion, are used to compare the proposed model and several existing software reliability models. Three datasets collected in industries are used to demonstrate the better fit of the proposed model than other existing software reliability models based on the studied criteria.

A Systematic Review of Software Reliability and Software Cost Models

Software reliability assessment is important to evaluate and predict the reliability and performance of software system. The models applicable to the assessment of software reliability are called Software Reliability Growth Models (SRGMs). An SRGM provides a mathematical relationship between time span of testing or using the software and the cumulative number of faults detected. It is used to assess the reliability of the software during testing and operational phases. An important class of SRGM that has been widely studied is Non Homogeneous Poisson Process (NHPP). NHPP models are useful in describing failure processes, providing trends such as reliability growth and fault-content. Different NHPP models have been developed for different applications. In this paper, we described several existing software reliability growth models based on Non Homogeneous Poisson processes (NHPPs). This paper also addresses cost estimation models and cost functions that can be used to evaluate the cost of software during its development. Keywords: Software Engineering; Software Reliability; SRGM; NHPP; MVF.

Estimation and Analysis of Some Generalized Multiple Change-Point Software Reliability Models

Software typically undergoes debugging during both a testing phase before product release, and an operational phase after product release. But it is noted that the fault detection and removal processes during software development and operation are different. For example, the fault removal during operation occurs generally at a slower pace than development. In this paper, we derive a powerful, easily deployable technique for software reliability prediction and assessment in the testing and operational phases. We first review how several existing software reliability growth models (SRGM) based on non- homogeneous Poisson processes (NHPP) can be readily derived from a unified theory. With the unified theory, we further incorporate the concept of multiple change-points, i.e. points in time when the software environment changes, into software reliability modeling. Several models are proposed and discussed under both ideal and imperfect debugging conditions. We estimate the parameters of the proposed models by employing real software failure data, and give a fair comparison with some existing SRGM. Numerical results show that the proposed models can provide good software reliability prediction in the various stages of software development and operation. Our approach is flexible; we can model various environments ranging from exponential-type to S-shaped NHPP models.

Predicting the types and locations of faults introduced during an imperfect repair process and their impact on reliability

Imperfect debugging of software development faults (called primary faults) will lead to the creation of new software faults denoted secondary faults. Secondary faults are typically fewer in numbers than the initial primary faults and are introduced late in the testing phase. As such it is unlikely that they will be observed during testing and their failure characteristics are unlikely to be assessed accurately. This is an issue since they may possibly display different propagation characteristics than the primary faults that led to their creation. In particular their location will be distributed non-uniformly around the fault being fixed. This paper proposes a methodology to assess the impact of secondary faults on reliability-based on predicting their possible types and locations. The methodology combines a fault taxonomy, code mutation and Bayesian statistics. The methodology is applied to portions of the application software code of a nuclear reactor protection system. This paper concludes with a discussion on the integration of the results within existing Software Reliability Growth Models.

A unified scheme of some nonhomogenous poisson process models for software reliability estimation

In this paper, we describe how several existing software reliability growth models based on Nonhomogeneous Poisson processes (NHPPs) can be comprehensively derived by applying the concept of weighted arithmetic, weighted geometric, or weighted harmonic mean. Furthermore, based on these three weighted means, we thus propose a more general NHPP model from the quasi arithmetic viewpoint. In addition to the above three means, we formulate a more general transformation that includes a parametric family of power transformations. Under this general framework, we verify the existing NHPP models and derive several new NHPP models. We show that these approaches cover a number of well-known models under different conditions.

An Investigation on the Occurrence of Service Requests in Commercial Software Applications

A proper characterization of the process of servicing requests for modifications in software systems and identification of the domain-specific aspects of this process are extremely important for software companies since service requests often consume lots of resources. A set of methods and criteria for modeling the servicing process is reviewed in this paper and the results of an extensive empirical investigation based on two commercial applications are presented. Existing software reliability growth models and gamma analysis are adopted to characterize the occurrence and typology of service requests over time. Linear regression is used to describe the learning process. A quantitative assessment is provided on the descriptive and predictive ability of the analyzed models. Since humans are involved in collecting the input data, the sensitivity of the proposed models to human errors is assessed using Monte Carlo simulation.

Effect of code coverage on software reliability measurement

Existing software reliability-growth models often over-estimate the reliability of a given program. Empirical studies suggest that the over-estimations exist because the models do not account for the nature of the testing. Every testing technique has a limit to its ability to reveal faults in a given system. Thus, as testing continues in its region of saturation, no more faults are discovered and inaccurate reliability-growth phenomena are predicted from the models. This paper presents a technique intended to solve this problem, using both time and code coverage measures for the prediction of software failures in operation. Coverage information collected during testing is used only to consider the effective portion of the test data. Execution time between test cases, which neither increases code coverage nor causes a failure, is reduced by a parameterized factor. Experiments were conducted to evaluate this technique, on a program created in a simulated environment with simulated faults, and on two industrial systems that contained tenths of ordinary faults. Two well-known reliability models, Goel-Okumoto and Musa-Okumoto, were applied to both the raw data and to the data adjusted using this technique. Results show that over-estimation of reliability is properly corrected in the cases studied. This new approach has potential, not only to achieve more accurate applications of software reliability models, but to reveal effective ways of conducting software testing.

Software reliability growth modeling based on testing-skill characteristics: Model and application

In order to test a software system, many test-cases are executed in the final stage of software development. Then, the set of modules and functions are influenced by the executed test-cases. The set is called the testing-domain and it expands with the progress of testing. The growth rate of the testing-domain in the software system is closely related to the quality and quantity of executed test-cases. Further, the quality of test-cases is related to the testing skill of test-case designers. In this paper, we discuss a software reliability growth model which describes the time-dependent growth behavior of the isolated testing-domain. This model based on a nonhomogeneous Poisson process is used for describing a software fault detection phenomenon during the software testing. Finally, this model is applied to fault data observed in actual development projects, the software reliability assessment results are shown, and comparisons of goodness-of-fit with existing software reliability growth models are performed. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(6): 42–49, 2001

Failure correlation in software reliability models

Perhaps the most stringent restriction in most software reliability models is the assumption of statistical independence among successive software failures. The authors research was motivated by the fact that although there are practical situations in which this assumption could be easily violated, much of the published literature on software reliability modeling does not seriously address this issue. The research work in this paper is devoted to developing the software reliability modeling framework that can consider the phenomena of failure correlation and to study its effects on the software reliability measures. The important property of the developed Markov renewal modeling approach is its flexibility. It allows construction of the software reliability model in both discrete time and continuous time, and (depending on the goals) to base the analysis either on Markov chain theory or on renewal process theory. Thus, their modeling approach is an important step toward more consistent and realistic modeling of software reliability. It can be related to existing software reliability growth models. Many input-domain and time-domain models can be derived as special cases under the assumption of failure s-independence. This paper aims at showing that the classical software reliability theory can be extended to consider a sequence of possibly s-dependent software runs, viz, failure correlation. It does not deal with inference nor with predictions, per se. For the model to be fully specified and applied to estimations and predictions in real software development projects, we need to address many research issues, e.g., the detailed assumptions about the nature of the overall reliability growth, way modeling-parameters change as a result of the fault-removal attempts.

Comparisons of optimal release policies for software systems

In a software product management, it is an important problem to determine the optimal release timing which minimizes the total expected cost incurred in both testing and operation phases. In this paper, we compare the performance between two kinds of software release methods, which are referred to as the T-policy and the N-policy. Based on the existing software reliability growth models, we formulate the expected cost functions and derive analytically the optimal policies under these control methods. Also, we derive a criterion for which control methods should be adopted. Finally, in numerical examples, we calculate the optimal release policies for several cost parameters.

Software reliability assessment for an exponential-S-shaped reliability growth phenomenon

Many software reliability growth models based upon an NHPP (nonhomogeneous Poisson process) have been proposed to measure and assess the reliability of a software system quantitatively. Generally, the error-detection rate per residual error during software testing is considered to be dependent on the elapsed time of testing. In this paper, to describe such a software reliability growth aspect realistically, we classify the software errors detected by the testing into two classes: some are easy to be detected and corrected and the other difficult to be detected. We describe the error-detection phenomena of two classes of errors as the dissimilar NHPP's. Then, we propose exponential-S-shaped software reliability growth models by superposing the reliability growth processes for the two error-detection phenomena. Finally, numerical illustrations of software reliability assessment are shown by applying the actual error data, and the model proposed here is compared among the existing software reliability growth models in terms of goodness-of-fit.

Test effort‐dependent software reliability growth models and comparisons of goodness‐of‐fit

AbstractTesting in software development is very important since it is the final refining stage before realizing the software to the user and requires a large amount of development resources. In this paper, the time‐dependent behavior of the test effort, such as the manpower spent in the testing process, is represented by exponential and Rayleigh functions, and the software reliability growth model is discussed, which includes the test‐effort functions. The nonhomogeneous Poisson process is assumed as the stochastic principle describing the software error‐detection phenomenon in the testing procedure. Especially, the variance analysis is discussed to determine the significance of the estimation of the test‐effort function by the least‐square method, as well as the prediction of the test effort invested at an arbitrary testing time. It is shown that the software reliability measure derived from the proposed model is a function of the invested test effort. Not only is the point estimation presented, but also the interval estimation. Examples are shown where the proposed model is applied to the actually observed test data, and the goodness‐of‐fit is compared with the existing software reliability growth models. As a result, the goodness‐of‐fit of the proposed model is verified, where the effect of the invested test effort is considered.

Optimal software release policies with simultaneous cost and reliability requirements

A cost-reliability optimal software release problem is investigated for three existing software reliability growth models. The decision policies on the optimum software release times are discussed by evaluating both software cost and software reliability criteria simultaneously.

Software Reliability Growth Modeling: Models and Applications

This paper summarizes existing software reliability growth models (SRGM's) described by nonhomogeneous Poisson processes. The SRGM's are classified in terms of the software reliability growth index of the error detection rate per error. The maximum-likelihood estimations based on the SRGM's are discussed for software reliability data analysis and software reliability evaluation. Using actual software error data observed by software testing, application examples of the existing SRGM's are illustrated.