A Bayes nonparametric framework for software-reliability analysis

Size, complexity, and human dependency on software--based products have grown dramatically during past decades. Software developers are struggling to deliver reliable software with acceptable level of quality, within given budget and schedule. One measure of software quality and reliability is the number of residual faults. Therefore, researchers are focusing on the identification of the number of fault presents in the software or identification of program modules that are most likely to contain faults. A lot of models have been developed using various techniques. A common approach is followed for software reliability prediction utilizing failure data. Software reliability and quality prediction is highly desired by the stakeholders, developers, managers, and end users. Detecting software faults early during development will definitely improve the reliability and quality in cost-effective way.KeywordsSoftware ReliabilityFault PredictionSoftware MetricsCapability Maturity ModelTest Case PrioritizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Over the past 30 years, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of software. In fact, effective debugging will not be easy because the fault may not be immediately obvious. In the past, some researchers ever used an infinite server queueing (ISQ) model to describe the software debugging behavior. An infinite-server queueing model is considered where access of customers to service is controlled by a gate and the gate is open only if all servers are free. However, the finite server queueing (FSQ) model is first advantageously modeled as an infinite-server system. Thus, in this paper, we will show how to incorporate both FSQ and ISQ models into software reliability estimation and prediction. In addition, here we also consider the factor of perfect/imperfect debugging. Experimental results show that the proposed framework to incorporate both fault detection and correction processes for SRGM has a fairly accurate prediction capability.

Research has shown that interest in knowledge facilitates students' academic achievement in learning. Because individual interest is often based on how much one knows, in other words existing or prior knowledge, studying adolescents' interest in health-enhancing physical activity and its benefits should address the relation between the interest and their existing or prior physical activity knowledge. Understanding this relationship may help us facilitate students to not only develop interest in knowing more about but also actual adopt a healthy, active lifestyle. This study used a large-sample structural equation design to identify the relationship between middle school students' interest in physical activity knowledge and their prior physical activity knowledge, and to assess the change of this relationship over time. Guided by the declarative-procedural knowledge framework, latent growth models were developed and tested on data collected from a random sample of 3882 students from ten middle schools. The latent growth curve model indicated that, 1) on average, students experienced a significant interest decline in both procedural and declarative knowledge; 2) prior knowledge helped slow the decline and facilitated interest growth in knowledgeable students. The results suggest that existing knowledge determined the interest change.

In this paper we consider non-parametric estimation methods for software reliability assessment without specifying the fault distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive approach based on the kernel estimation is provided with several kernel functions and bandwidth estimations. Next, we develop interval estimation methods via the non-parametric bootstrap, and derive the confidence regions of several reliability measures such as the expected cumulative number of software faults, software intensity function, quantitative software reliability as well. The resulting data-driven methodology can give the useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault distribution. In illustrative examples with a real software fault data, it is shown that the proposed methods provide useful software reliability measures under uncertainty from the view point of frequentist analysis.

In reality, the fault detection (or correction) phenomenon and software reliability estimation in the operational phase are different from that in the testing phase. The fault removal continues at a slower rate during the operational phase. In this paper, we will investigate some techniques for software reliability prediction and measurement in the operational phase. We first review how some software reliability growth models (SRGMs) based on non-homogeneous Poisson processes (NHPPs) can be readily derived based on a unified theory for NHPP models. Under this general framework, we can not only verify some conventional SRGMs but also derive some new SRGMs that can be used for software reliability measurement in the operational phase. That is, based on the unified theory, we can incorporate the concept of multiple changepoints into software reliability modeling. We can formularize and simulate the fault detection process in the operational phase. Some numerical illustrations based on real software failure data are also presented. The experimental results show that the proposed models can easily reflect the possible changes of fault detection process and offer quantitative analysis on software failure behavior in field operation.

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The development of a user-oriented procedure for software reliability prediction of telecommunications systems is proposed. The procedure is based on software reliability data that encapsulate the characteristics of the software decomposed into comparable units, as well as the more traditional failure data used in most of the current models. The state-of-the-art in software reliability prediction is reviewed. The software characteristics and the software reliability models that could form the backbone of a procedure are described together with some of the software reliability issues that have to be resolved for a useful prediction procedure for telecommunications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Predicting Reliability of Software using SRGMs are a big challenge for Software Engineers. Software Reliability is a way to find the probability of software to operate in a given time limit and in a specified environment without causing any failure; hence it is reviewed as Quantifiable Metric. System faults can be the result of software or hardware errors. Software fault is comparatively more difficult to predict than hardware faults. Even the Reliability of a web application is also hard to determine due to its highly distributed nature. Predicting the reliability of application helps the Engineers to compute the software's release date/time and to manage various resources of software such as people, money, time etc. In this paper SRGM Models are considered namely Goel Okumoto Model (GO Model), Yamada S-shaped Model and Kapur & Garg Model (KG Model) to estimate and predict Software Reliability by detecting the cumulative number of faults in software application within specified time. Additional number of days are also calculated to remove them and release the software on time. By implementing the test cases of actual defects per day, Rate of Change or Test Case Efficiency can be measured.

The importance of software systems and their impact on all sectors of society is undeniable. Furthermore, it is increasing every day as more services get digitized. This necessitates the need for evolution of development and quality processes to deliver reliable software. For reliable software, one of the important criteria is that it should be fault-free. Reliability models are designed to evaluate software reliability and predict faults. Software reliability prediction is always an area of interest in the field of software engineering. Prediction of software reliability can be done using numerous available models but with the inception of computational intelligence techniques, researchers are exploring new techniques such as machine learning, genetic algorithm, deep learning, etc. to develop better prediction models. In the current study, a software reliability prediction model is developed using a deep learning technique over twelve real datasets from different repositories. The results of the proposed model are analyzed and found quite encouraging. The results are also compared with previous studies based on various performance metrics.

Software quality can be improved by early software defect prediction models. However, class imbalance due to under representation of defects and the irrelevant metrics used to predict them are two major challenges that hinder the model performance. This article presents a new two-stage framework of Ensemble of Hybrid Feature selection (EHF) with Weighted Support Vector Machine Boosting (WSVMBoost), which further enhance the model performance. The EHF is the ensemble feature ranking of feature selection models such as filters and embedded models to select the relevant metrics. The classification ensembles, namely Random Forest, RUSBoost, WSVMBoost, and the base learners, namely Decision Tree, and SVM are also explored in this study using five software reliability datasets. From the statistical tests, EHF with WSVMBoost attained best mean rank in terms of performance than the rest of the feature selection hybrids in predicting the software defects. Additionally, this study has shown that both McCabe and Hasalted method level metrics are equally important in improving the model performance.

Software reliability is one of the key aspects of software quality estimation and prediction during software testing period. Hence, accurate prediction of software reliability is an important but critical job. Machine Learning (ML) techniques have been proven victorious in providing superior results than traditional techniques for software reliability prediction. Generally, ML models entail sufficient data for training to achieve improved generalization. Inadequate training data may lead to land at suboptimal solution. This article suggests a method of enriching training dataset through exploration and incorporation of virtual data from existing data. For boost up the overall accuracy and reducing the risk of model selection an ensemble framework of five ML methods is suggested. The extended software reliability dataset is then exposed to the constituent models as well as the ensemble approach separately to estimate the future data. Extensive simulation results on a couple of software reliability datasets reveal that our proposed model significantly improves the prediction accuracy.

Assessment &amp;amp; Prediction of Software Reliability: ANN Approach

Cray Research Inc. is using the Poisson-type models proposed by A.C. Goel (1985), K. Okumoto (1985), J.D. Musa (1987), M. Obah (1984). Because the processes fail to conform to these models, they have been of little use in predicting software reliability or failure intensity for products which have yet to be released. This led to the search for non-parametric or non-model dependent approaches. Using sequential analysis Cray have been able to test hypotheses by comparing an unspecified model to a true homogeneous Poisson process operating at a target rate. >

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Software Reliability Growth Models (SRGMs) provide techniques to predict future failure behavior from known characteristics of the software testing work. However, in some cases, software developers did not have sufficient historical data to estimate the corresponding reliability and the expected testing cost, especially for a newly developed software project, and thus the results obtained from analytical models may not be reliable. In such situations, Bayesian analysis is a reasonable approach to additionally take expert's opinions into account for better decision making. In this paper, we utilized Yamada Delayed S-shaped Model with Bayesian analysis in predicting software reliability and expected testing costs to determine an optimal release time for software systems. Besides, the failure process of software are assumed to be drawn from a non-homogeneous Poisson process (NHPP), and the parameters of the proposed model are assumed to be mutually independent and Gamma distributed. Finally, a numerical example is given to verify the effectiveness of the proposed approach, and the sensitive and risk analyses are performed in light of the numerical example.