Software Reliability Growth Models Research Articles

Optimal Allocation Based on Cost, Testing Effort and Reliability

In this paper, we investigate a software resource allocation problem. We incorporate generalized modified weibull testing effort function in software reliability growth model to get a better description of the software debugging process. The main purpose is to minimize the cost of software development when the number of remaining faults and a desired reliability objective are given. A real software failure project is demonstrated the effectiveness of proposed models.

Queueing-Theory-Based Models for Software Reliability Analysis and Management

Software reliability is one of the most important internal attributes of software systems. Over the past three decades, many software reliability growth models have been proposed and discussed. Some research has also shown that the fault detection and removal processes of software can be described and modeled using an infinite-server queueing system. But, there is practically no company that can afford unlimited resources to test and correct detected faults in the real world. Consequently, the number of debuggers should be limited, not infinite. In this paper, we propose an extended finite-server-queueing (EFSQ) model to analyze the fault removal process of the software system. Numerical examples based on real project data are illustrated. Evaluation results show that our proposed EFSQ model has a fairly accurate prediction capability of software reliability and also depict the real-life situation of software development activities more faithfully. Finally, the applications of our proposed model to project management are also presented. Our proposed model can provide a theoretically effective technique for managing the necessary activities of testing and debugging in software project management.

Comparing the reliability of software systems: A case study on mobile operating systems

Assessment of software reliability is inevitable in modern software production process. Many works aimed at better models for measurement and prediction of reliability of software products. Tens of approaches have been developed and evaluated so far. However, very few works focus on approaches to compare existing systems with respect to reliability. Despite a tremendous importance to practice (and software management area), a complete and sound comparison methodology does not exist. In this paper, we propose a methodology for software reliability comparison. The methodology extensively applies the GQM approach and software reliability growth models. The methodology has been thoroughly evaluated on a case of assessment and comparison of three open source mobile operating systems: Sailfish, Tizen and CyanogenMod.

Stochastic Formulation of Fault Severity Based Multi Release SRGM Using the Effect of Logistic Learning

In today’s environment, software reliability is one of the major concerns for Software firms. Many Software Reliability Growth Model (SRGM) has been developed and many are under process. In order to meet the requirements of consumer and to excel in competitive environment, companies are coming up with multiple add –ons. We design the model as stochastic with continuous state space because of large software system, the count of failures observed is huge and so, the variation in count of errors detected/ removed in each debugging is petite compared to original error content at the beginning of testing. This study is an add on to the software reliability literature where we have developed multi release SRGM’s based on available concept of depending on previous releases. The errors have been categorically divided upon the severity of their removal as one stage, two stage, three stage fault removal process is applied in an environment of irregular fluctuations.

Testing Domain Dependent Software Reliability Growth Models

Software Reliability Growth Models (SRGMs) are supporting software industries in expecting and scrutinizing quality of software. Numerous SRGMs have been proposed; majority of which concentrate on testing period of software. For testing, domain specific knowledge plays a very crucial role. Based on necessity condition, a set of programmes are in testing phase of software development. “Domain testing is a software technique in which small number of test cases is selected for trial. These sets of testing paths, all of which are to be eventually influenced by designed test cases are called the testing domain which expands with the progress of testing”. Keeping this concept in mind, we propose SRGMs with the concept of testing domain with exponential coverage. Utility of proposed framework has been emphasized in this paper through some models pertaining to different distribution i.e Exponential, Logistic, Weibull and Rayleigh. Moreover, the data analysis is performed to find the estimates of parameters by fitting the models on authentic data sets.

Performance Optimized Expectation Conditional Maximization Algorithms for Nonhomogeneous Poisson Process Software Reliability Models

Nonhomogeneous Poisson process (NHPP) and software reliability growth models (SRGM) are a popular approach to estimate useful metrics such as the number of faults remaining, failure rate, and reliability, which is defined as the probability of failure free operation in a specified environment for a specified period of time. We propose performance-optimized expectation conditional maximization (ECM) algorithms for NHPP SRGM. In contrast to the expectation maximization (EM) algorithm, the ECM algorithm reduces the maximum-likelihood estimation process to multiple simpler conditional maximization (CM)-steps. The advantage of these CM-steps is that they only need to consider one variable at a time, enabling implicit solutions to update rules when a closed form equation is not available for a model parameter. We compare the performance of our ECM algorithms to previous EM and ECM algorithms on many datasets from the research literature. Our results indicate that our ECM algorithms achieve two orders of magnitude speed up over the EM and ECM algorithms of [1] when their experimental methodology is considered and three orders of magnitude when knowledge of the maximum-likelihood estimation is removed, whereas our approach is as much as 60 times faster than the EM algorithms of [2] . We subsequently propose a two-stage algorithm to further accelerate performance.

Multi Up-gradation Software Reliability Growth Model Considering the Joint Effect of Testing and Operational Phase

Multi Up-gradation Software Reliability Growth Model Considering the Joint Effect of Testing and Operational Phase

A testing-coverage software reliability model considering fault removal efficiency and error generation.

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

Debugging‐workflow‐aware software reliability growth analysis

SummarySoftware reliability growth models support the prediction/assessment of product quality, release time, and testing/debugging cost. Several software reliability growth model extensions take into account the bug correction process. However, their estimates may be significantly inaccurate when debugging fails to fully fit modelling assumptions. This paper proposes debugging‐workflow‐aware software reliability growth method (DWA‐SRGM), a method for reliability growth analysis leveraging the debugging data usually managed by companies in bug tracking systems. On the basis of a characterization of the debugging workflow within the software project under consideration (in terms of bug features and treatment phases), DWA‐SRGM pinpoints the factors impacting the estimates and to spot bottlenecks, thus supporting process improvement decisions. Two industrial case studies are presented, a customer relationship management system and an enterprise resource planning system, whose defects span a period of about 17 and 13 months, respectively. DWA‐SRGM revealed effective to obtain more realistic estimates and to capitalize on the awareness of critical factors for improving debugging.

NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

• The uncertainty of operating environments and imperfect debugging are considered. • Fault detection rate is based on testing coverage and the model is based on NHPP. • The performance of the proposed model is compared with other 15 existing models. • Optimal software release time and its sensitivity analysis are discussed based on cost and reliability. • Seven criteria and improved normalized criteria distance method are used in the experiments. In this paper, we propose a testing-coverage software reliability model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP). Software is usually tested in a given control environment, but it may be used in different operating environments by different users, which are unknown to the developers. Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures, but most of the underlying common assumptions of these models are that the operating environment is the same as the developing environment. But in fact, due to the unpredictability of the uncertainty in the operating environments for the software, environments may considerably influence the reliability and software's performance in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the theory reliability, and also from all its similar applications in other fields. In this paper, a new model is proposed with the consideration of the fault detection rate based on the testing coverage and examined to cover ID subject to the uncertainty of operating environments. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real software failure data based on seven criteria. Improved normalized criteria distance (NCD) method is also used to rank and select the best model in the context of a set of goodness-of-fit criteria taken all together. All results demonstrate that the new model can give a significant improved goodness-of-fit and predictive performance. Finally, the optimal software release time based on cost and reliability requirement and its sensitivity analysis are discussed.

The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models

An Ideal Software Release Policy for an Improved Software Reliability Growth Model Incorporating Imperfect Debugging with Fault Removal Efficiency and Change Point

This paper presents a general software reliability growth model (SRGM) based on non-homogeneous Poisson process (NHPP) and optimal software release policy with cost and reliability criteria. The main motive of this study is to develop a software release time decision model considering maintenance cost and warranty cost under fuzzy environment. In previous studies, maintenance cost has been defined either in terms of warranty cost or fault debugging cost. In reality, maintenance cost includes the cost of free patches, updates, technical support and future enhancement. Also, it is possible that maintenance process causes the removal of software faults in the operational phase including the faults which occur outside the warranty period or warranty definition. In other words, warranty action may be included the maintenance action, but not the converse. Considering this fact, maintenance cost and warranty cost are defined separately in the proposed study. Initially, an SRGM has been proposed with the revised concept of imperfect debugging phenomenon considering fault removal efficiency (FRE). Furthermore, the effect of changes in various environmental factors on models parameters has been taken into account. Numerical examples based on real software failure data sets have been given to analyze the performance of the proposed models.

Evaluation of Project Architecture in Software Development Mixing Waterfall and Agile by Using Process Simulation

For software development, especially massive software systems, a waterfall process is used traditionally. A waterfall process can be highly effective on the condition that a master plan is fixed and the possibility of changes and uncertain rework is low. However, in software development projects, many kinds of reworks occur corresponding to uncertain requirement changes and program bugs. In addition, with the advent of cloud-based software platforms and continuous development operations, it is possible to develop a software system while operating the system. To respond to this situation, software development projects often adopt an agile process. Agility may allow conditional response to uncertain rework, yet at the same time it may be difficult to control the achievement of known project targets. Recently, many cases of adopting mixed processes including waterfall and agile have been reported in the massive software development projects. In this paper, we argue that the mixed process architecture should be designed, considering the scale of the targeted software project, the culture of organization, the probability of uncertain requirement changes, and so on. This paper proposes a methodology of evaluating the impact of waterfall, agile, and mixed project architectures by using process simulation. A project architectural approach is evaluated with a simulator which includes a software reliability growth model and uncertain rework driven by requirement change and error propagation. The proposed methodology was applied to a development project for a simple shopping website. The results showed that the proposed methodology allows exploration of partial agile adoption depending on the nature of the system development project, including its scale and chances of change. For example, in this paper, if the scale of the project is small, the positive effect of increasing agility by adopting agile processes is low. On the other hand, if the scale of the project is large, the effect of increasing agility by adopting agile process can increase. Furthermore, it became clear that it is important to not apply an agile process blindly, but instead to design a mixed project architecture considering the number of errors and development schedule targets across the project scope.

An Efficient Particle Swarm Optimization-Based Neural Network Approach for Software Reliability Assessment

In this paper, an artificial neural network (ANN)-based dynamic weighted combination model trained by novel particle swarm optimization (PSO) algorithm is proposed for software reliability prediction. Different software reliability growth models (SRGMs) are merged based on the weights derived by the learning algorithm of the proposed ANN. To avoid trapping in local minima during training of the ANN, we propose a neighborhood-based adaptive PSO (NAPSO) algorithm for learning of the proposed ANN in order to find global optimal weights. We conduct the experiments on real software failure data sets for validation of the proposed dynamic weighted combination model (PDWCM). Fitting performance and predictability of the proposed PSO-based neural network are compared with the conventional PSO-based neural network (CPSO) and existing ANN-based software reliability models. We also compare the performance of the proposed PSO algorithm with the CPSO algorithm through learning of the proposed ANN. Empirical results indicate that the proposed PSO and CPSO-based neural network present fairly accurate fitting and prediction capability than the other existing ANN-based software reliability models. Moreover, the proposed PSO-based neural network is most promising for the purpose of software fault prediction since it shows comparatively better fitting and prediction performance results than the other models.

An Adaptive EM Algorithm for the Maximum Likelihood Estimation of Non-Homogeneous Poisson Process Software Reliability Growth Models

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMa) enable quantitative metrics to guide decisions during the software engineering life cycle, including test resource allocation and release planning. However, many SRGM possess complex mathematical forms that make them difficult to apply. Specifically, traditional procedures solve a system of nonlinear equations to identify the numerical parameters that best characterize failure data. Recently, researchers have developed expectation-maximization (EM) algorithms for NHPP SRGM that exhibit better convergence properties and can therefore find maximum likelihood estimates with greater ease.This paper presents an adaptive EM (AEM) algorithm, which combines an earlier EM algorithm for NHPP SRGM with unconstrained search of the model parameter space. Our performance analysis shows that the AEM outperforms state-of-the-art EM algorithms for NHPP SRGM with very strong statistical significance, which is as much as hundreds of times faster on some data sets. Thus, the approach can fit SRGM very quickly. We also incorporate this high performance adaptive EM algorithm into a heuristic nested model selection procedure to objectively select a model of least complexity that best characterizes the failure data. Results indicate this heuristic approach often identifies the model possessing the best model selection criteria.aAcronyms are not pluralized.

Testing Domain based Software Reliability Growth Models using Stochastic Differential Equation

Testing Domain based Software Reliability Growth Models using Stochastic Differential Equation

Reliability analysis for multi-release open-source software systems with change point and exponentiated Weibull fault reduction factor

Evolutions in technology and rapid growth of open-source software (OSS) applications have made upgradation indispensable. To incorporate vagaries in users needs and environmental situations that affect the reliability growth of OSS, fault reduction factor (FRF) is one of the important factors to be considered during multi-release software development process. In this paper, a software reliability growth model (SRGM) has been proposed for successive releases to include some of the critical but realistic characteristics such as time variant FRF, effect of change point due to changing strategies and error generation during fault removal phenomena. For model validation, two popular open-source projects—Mozilla and Gnome—have been chosen for its multi-release real data sets. An optimal updation schedule problem is formulated, and a numerical illustration has been worked out. The goodness of fit of the proposed model is shown by comparing it with one of the well-known models reported in the literature.

Estimating the parameters of software reliability growth models using hybrid DEO-ANN algorithm

The parameter estimation of software reliability growth model (SRGM) is extremely useful for software developers and has been broadly acknowledged and applied. However, each SRGM contains some undetermined parameters and estimation of these parameters is a fundamental task. Mostly, these parameters are estimated by the least square estimation (LSE) or the maximum likelihood estimation (MLE). However, these methods impose certain constraints on the parameter estimation of SRGM like requiring the modelling function. In this paper, we propose an efficient approach to estimate the parameters of SRGM using a hybrid dolphin echolocation optimisation-artificial neural network (DEO-ANN) through parallel computation. The DEO is utilise to optimise the weights and the structure of the ANN to reduce computational complexity. The performance of the proposed approach for parameter estimation of SRGM is also compared with other existing approaches. The experimental results show that the proposed parameter estimation approach using DEO-ANN is very effective and flexible, and the better software reliability growth performance can be obtained on the different software failure datasets.

Improving software reliability estimation using multi-layer neural-network combination model

Software reliability growth models (SRGM) can be used to derive quantitative information about the quality of the product and also to estimate the software testing time needed to achieve target reliability. The modelling performance of SRGMs depends on the nature of the failure data set. In this paper, we propose a multi-layer feed-forward artificial neural network (ANN) combination model using two widely used generalised SRGMs as base models with back-propagation training algorithm. The proposed ANN-based model is compared with the traditional SRGMs and a few ANN based software reliability models. The performance comparison seems to confirm that, the goodness of fit and the predictive validity of proposed model appears to be better than that of reliability models compared. The proposed ANN-based model provides consistent performance for both exponential, and S-shaped growth of mean value function witnessed in software projects.

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.