Software Failure Data Research Articles

Fairness evaluation method of resource allocation based on BPSO multidimensional perspective

In order to improve the effectiveness of fairness evaluation algorithm for public health resources, a kind of fairness evaluation method for public health resources based on BPSO dimension reduction under multi-perspective was proposed in the thesis. Firstly, data sources and research methods for fairness evaluation of public health resources was introduced; computational steps of Lorenz index were given; then, in order to reduce the complexity of data processing, particle swarm optimization was used to implement dimension reduction processing for software failure data; character string (0 or 1) of binary system was used to show particle position so as to realize simplification of data processing. Finally, effectiveness of methods mentioned in the thesis is verified in the positive analysis of fairness evaluation for regional distribution of health resources in Shaoguan city, Guangdong province.

Weighted Nonlinear Least Squares Technique for Parameters Estimation of the NHPP Gompertz Model

With the problem of heteroscedasticity an alternative precise estimation method of the nonlinear least squares (NLS) technique is needed. Weighted nonlinear least squares estimation (WNLSE) technique is an alternative that may increase the accuracy of parameters estimation by assigning suitable weights to the time between failures data. In the present study, the traditional maximum likelihood (ML), nonlinear least squares (NLS), and weighted nonlinear least squares (WNLS) techniques are formulated to estimate the three parameters of the NHPP Gompertz model. Empirical weighting method is investigated in NHPP Gompertz model prediction process. Three real software failure data examples are provided to analyze the performance of the three considered methods of estimation. The results of this numerical study indicate the preferences to the WNLSE method with respect to the NHPP Gompertz model’s performance, also the value of the weighting factors which give the optimum solution differ according to the nature of software failure data.

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.

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.

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.

Release and testing stop time of a software using multi-attribute utility theory

Almost all existing studies consider that product release results in the end of testing phase. However, the modern practices in software industry perceive a post release testing during which organization treats the remaining bugs present in the software to improve product experience for customer and at the same time trying to lower down the possibilities of software failure before testing ends. However, continuing testing after software release is not considered cost-effective for firms. Hence, it is essential to decide the optimal testing stop time under the budget. We comprehend this scenario to design a framework for optimal scheduling policy by considering cost and reliability as two attribute using multi-attribute utility theory. Validation of the obtained results is done on real-life software failure data set of tandem computers.

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.

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

Software Quality Control Truncated Time with Linear Failure Rate Distribution based on NHPP

Statistical process control is a method of monitoring product in its development process using statistical techniques with the presumption that the products produced under identical process condition shall not always be alike with respect to some quality characteristic(s). However, if the observed variations are with in the tolerable limits statistical process control (SPC) methods would pass them for acceptance. This philosophy is adopted to decide the reliability and quality of a developed software by defining some quality measures and proposing a probability model for the quality measurements. The well known linear failure rate distribution(LFRD) is considered to propose a software reliability based on non-homogenous Poisson process (NHPP). Its mean value function is taken as a quality characteristic and SPC limits for it are developed. These control limits are exemplified to a live failure data to detect the out of control signals for the quality of the software based on the software failure data.

Maximum-Likelihood Estimation of Parameters of NHPP Software Reliability Models Using Expectation Conditional Maximization Algorithm

Since its introduction in 1977, the expectation maximization (EM) algorithm has been one of the most important and widely used estimation method in estimating parameters of distributions in the presence of incomplete information. In this paper, a variant of the EM algorithm, the expectation conditional maximization (ECM) algorithm, is introduced for the first time and it provides a promising alternative in estimating the parameters of nonhomogeneous poisson (NHPP) software reliability growth models (SRGM). This algorithm circumvents the difficult M-step of the EM algorithm by replacing it by a series of conditional maximization steps. The utility of the ECM approach is demonstrated in the estimation of parameters of several well-known models for both time domain and time interval software failure data. Numerical examples with real-data indicate that the ECM algorithm performs well in estimating parameters of NHPP SRGM with complex mean value functions and can produce a faster rate of convergence.

Lehmann-Type Laplace distribution-Type I software reliability growth model

In this paper, Lehmann-Type Laplace Type I reliability growth model is proposed for early detection of software failure based on time between failure observations. Cumulative time between failures of the software data is assumed to follow Lehmann-Type Laplace distribution-Type I (LLD-Type I). The parameters are estimated using Profile Likelihood Method. In terms of AIC and BIC, this distribution is found to be a better fit for the software failure data than Goel–Okumoto, Weibull, Pareto Type III and Kumaraswamy Modified Inverse Weibull distributions which are commonly used in reliability analysis. A LLD-Type I control mechanism is used to detect the failure points of a software data.

Monitoring Software Failure Process using Half Logistic Distribution

In this paper, Software reliability is the anticipation of operations which are free of error in the software in a stated environment during the detailed time duration. Statistical Process Control can survey the gauging of software failure and thereby devote significantly to the enhancement of software reliability. Such an assessment assists the software development team to pinpoint and diagnose their actions during software failure process and hence, assure superior software reliability. A control mechanism planted on the cumulative observations of interval domain failure data using mean value function of the Half Logistic Distribution (HLD) based on Non Homogeneous Poisson Process (NHPP) is proposed. The maximum likelihood estimation approach is used to estimate the unknown parameters of the model. A new mechanism is coded to analyze the observations instead of using regular control charts. General Terms Decision Rule, Software testing, Software failure data, Quality Software.

A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization

reliability is one of the key attributes to determine the quality of a software system. Finding and minimizing the remaining faults in software systems is a challenging task. Software reliability growth model (SRGM) with testing-effort function (TEF) is very helpful for software developers and has been widely accepted and applied. However, each SRGM with TEF (SRGMTEF) contains some undetermined parameters. Optimization of these parameters is a necessary task. Generally, these parameters are estimated by the Least Square Estimation (LSE) or the Maximum Likelihood Estimation (MLE). However, the software failure data may not satisfy such a distribution. We investigate the improvement and application of a swarm intelligent optimization algorithm, namely Modified Genetic Swarm Optimization algorithm, to optimize these parameters of SRGMTEF. The performance of the proposed SRGMTEF model with optimized parameters is also compared with other existing models Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The experiment results show that the proposed parameter optimization approach using Modified Genetic Swarm Optimization is very effective and flexible, and the better software reliability growth performance can be obtained based on SRGMTEF on the different software failure datasets. Also, provided comparison of ten SRGMs ( Like Goel-Okumoto Model, Delayed S-shaped Growth Model, Yamada Imperfect Debugging Models, Yamada Rayleigh Model, Inflection S-shaped Model…..etc).

Software fault prediction using neuro-fuzzy network and evolutionary learning approach

In the real world, a great deal of information is provided by human experts that normally do not conform to the rules of physics, but describe the complicated systems by a set of incomplete or vague statements. The need of conducting uncertainty analysis in software reliability for the large and complex system is demanding. For large complex systems made up of many components, the uncertainty of each individual parameter amplifies the uncertainty of the total system reliability. In this paper, to overcome with the problem of uncertainty in software development process and environment, a neuro-fuzzy modeling has been proposed for software fault prediction. The training of the proposed neuro-fuzzy model has been done with genetic algorithm and back-propagation learning algorithm. The proposed model has been validated using some real software failure data. The efficiency of the two learning algorithms has been compared with various fuzzy and statistical time series-based forecasting algorithms on the basis of their prediction ability.

Optimization Problems for a Modular Software with Faults of Different Severity

For the past several decades, reliability has been presumed as the most important characteristic of any complex software system. Software developer’s assurance of high quality and reliable software is driven on the basis of efficient reliability assessment. Since 1970’s an unprecedented growth has been observed in the area of software reliability growth modeling. Software reliability growth models (SRGM) are linked to the testing stage of software development and provides for insights into ways to improve reliability of the system and optimal time to release the software. Several SRGMs have been proposed in the literature to model fault identification/removal phenomenon based on time; search for more efficient and accurate models that can fit greater number of reliability growth curves is endless. Categorization of faults lying in the software has been widely studied as well. Also, efforts have been made in the past to understand reliability issues concerned with modular software system. In this paper we examine three different types of faults lying in complex software and study their behavior in an N-module software system. In order to attain two fold objectives of maximizing reliability of such a system and minimizing overall debugging cost, we propose two related optimization models. The inbuilt model flexibility takes care of different environment. The model is validated by real life software failure data set to show its goodness of fit and applicability.

Incorporating Features Enhancement Archetype in Software Reliability Growth Modeling and Optimal Release Time Determination

Requirement Analysis is one of the important phases of any Software Development Life Cycle (SDLC). The competitive market scenario has made clients dynamic and informative and so in between demand of changing or addition of new functionalities is a very common scenario that firms faces now-a-days. But this up-gradation of the system often brings new complexities that might alter/increase the total fault count in the software system. The add-ons or the interfacing of the modules with other applications is cumbersome task and adds to the load of the testing team. Taking this feature intensification affect (after a certain time point) into account, in this paper, an optimal release time of the software has been identified. The proposal has been validated on real life software failure data set and results show the impact of feature enhancement.

Economic Impact of Software Patching and Optimal Release Scheduling

Because of highly distributed nature of software products, the task of improving software reliability is becoming a complex job. Specialized tools and techniques are being used to isolate the risky software modules. In order to retain in market, firms are required not only to provide the software on time but also to endow with continuous processing. Product updating is the process that comes to safeguard the firm's image at this point of time. And one such attribute of updating is providing software patches. Today, almost all software firms are providing either patching, module exchange or service pack application processes consequent to a release. This paper proposes a scheduling policy for a software product and shows the importance of patching in lowering the system outages and making the system more cost effective. Validation on the proposed policy has been done using real life software failure data set of Tandem Computers. Copyright © 2016 John Wiley & Sons, Ltd.

트렌드와 고장 예측 능력을 반영한 소프트웨어 신뢰도 성장 모델 선택 방법

소프트웨어 신뢰도 성장 모델은 소프트웨어 신뢰도를 정량적으로 평가하기 위해서 사용되며 고장 데이터를 사용해서 소프트웨어 출시일 또는 추가 테스트 노력을 결정하기 위해서도 사용된다. 특정 소프트웨어 신뢰도 성장 모델을 모든 소프트웨어에 사용할 수 없기 때문에 평가 대상 소프트웨어에 가장 잘 맞는 소프트웨어 신뢰도 성장 모델을 선택하는 것이 중요한 이슈가 되었다. 기존 소프트웨어 신뢰도 성장 모델 선택 방법은 수집된 고장 데이터에 대한 소프트웨어 신뢰도 성장 모델의 적합도만을 평가하며 앞으로 발생할 고장 예측의 정확도는 고려하지 않는다. 이 논문에서는 고장 데이터의 트렌드와 고장 예측능력을 반영한 소프트웨어 신뢰도 성장 모델 선택 방법을 제안한다. 연구의 타당성을 보이기 위하여 실험을 통해서 기존 소프트웨어 신뢰도 성장 모델 선택 방법의 문제점을 확인하고 이 논문에서 제안하는 소프트웨어 신뢰도 성장 모델 선택 방법을 사용하면 기존 방법에 비해 더 정확한 고장 예측을 하는 신뢰도 모델을 선택할 수 있음을 보인다.

Parameter optimization of software reliability growth model with S-shaped testing-effort function using improved swarm intelligent optimization

Software reliability growth model (SRGM) with testing-effort function (TEF) is very helpful for software developers and has been widely accepted and applied. However, each SRGM with TEF (SRGMTEF) contains some undetermined parameters. Optimization of these parameters is a necessary task. Generally, these parameters are estimated by the Least Square Estimation (LSE) or the Maximum Likelihood Estimation (MLE). We found that the MLE can be used only when the software failure data to satisfy some assumptions such as to satisfy a certain distribution. However, the software failure data may not satisfy such a distribution. In this paper, we investigate the improvement and application of a swarm intelligent optimization algorithm, namely quantum particle swarm optimization (QPSO) algorithm, to optimize these parameters of SRGMTEF. The performance of the proposed SRGMTEF model with optimized parameters is also compared with other existing models. The experiment results show that the proposed parameter optimization approach using QPSO is very effective and flexible, and the better software reliability growth performance can be obtained based on SRGMTEF on the different software failure datasets.

Determining the optimal allocation of testing resource for modular software system using dynamic programming

Reliability is a major concern in the process of software development because unreliable software can cause failure in the computer system that can be hazardous. A way to enhance the reliability of software is to detect and remove the faults during the testing phase, which begins with module testing wherein modules are tested independently to remove a substantial number of faults within a limited resource. Therefore, the available resource must be allocated among the modules in such a way that the number of faults is removed as much as possible from each of the modules to achieve higher software reliability. In this article, we discuss the problem of optimal resource allocation of the testing resource for a modular software system, which maximizes the number of faults removed subject to the conditions that the amount of testing-effort is fixed, a certain percentage of faults is to be removed and a desired level of reliability is to be achieved. The problem is formulated as a non linear programming problem (NLPP), which is modeled by the inflection S-shaped software reliability growth models (SRGM) based on a non homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing-effort functions. A solution procedure is then developed using a dynamic programming technique to solve the NLPP. Furthermore, three special cases of optimum resource allocations are also discussed. Finally, numerical examples using three sets of software failure data are presented to illustrate the procedure developed and to validate the performance of the strategies proposed in this article. Experimental results indicate that the proposed strategies may be helpful to software project managers for making the best decisions in allocating the testing resource. In addition, the results are compared with those of Kapur et al. (2004), Huang and Lyu (2005), and Jha et al. (2010) that are available in the literature to deal the similar problems addressed in this article. It reveals that the proposed dynamic programming method for the testing-resource allocation problem yields a gain in efficiency over other methods.