Improving software reliability prediction through multi-criteria based dynamic model selection and combination

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.

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>

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.

In the software industry, two software engineering development best practices coexist: open-source and closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a proprietary code that only the owner can access. Software reliability is crucial in the industry when a new product or update is released. Applying meta-heuristic optimization algorithms for closed-source software reliability prediction has produced significant and accurate results. Now, open-source software dominates the landscape of cloud-based systems. Therefore, providing results on open- source software reliability - as a quality indicator - would greatly help solve the open-source software reliability growth- modelling problem. The reliability is predicted by estimating the parameters of the software reliability models. As software reliability models are inherently nonlinear, traditional approaches make estimating the appropriate parameters difficult and ineffective. Consequently, software reliability models necessitate a high- quality parameter estimation technique. These objectives dictate the exploration of potential applications of meta- heuristic swarm intelligence optimization algorithms for optimizing the parameter estimation of nonhomogeneous Poisson process-based open-source software reliability modelling. The optimization algorithms are firefly, social spider, artificial bee colony, grey wolf, particle swarm, moth flame, and whale. The applicability and performance evaluation of the optimization modelling approach is demonstrated through two real open-source software reliability datasets. The results are promising

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.

Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter on software reliability prediction using ANNs addresses three main issues: (1) analyze, manage, and improve the reliability of software products; (2) satisfy the customer needs for competitive price, on time delivery, and reliable software product; (3) determine the software release instance that is, when the software is good enough to release to the customer.

In this paper, we propose an artificial neural- network-based approach for software reliability estimation and modeling. We first explain the network networks from the mathematical viewpoints of software reliability modeling. That is, we will show how to apply neural network to predict software reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model. The applicability of proposed model is demonstrated through four real software failure data sets. From experimental results, we can see that the proposed model significantly outperforms the traditional software reliability models.

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.

Models for predicting software reliability in the early phases of development are of paramount importance since they provide early identification of cost overruns, software development process issues, optimal development strategies, etc. A few models geared towards early reliability prediction, applicable to well defined domains, have been developed during the 1990s. However, many questions related to early prediction are still open, and more research in this area is needed, particularly for developing a generic approach to early reliability prediction. This paper presents an approach to predicting software reliability based on a systematic identification of software process failure modes and their likelihoods. A direct consequence of the approach and its supporting data collection efforts is the identification of weak areas in the software development process. A Bayes framework for the quantification of software process failure mode probabilities can be useful since it allows use of historical data that are only partially relevant to the software at hand. The key characteristics of the approach should apply to other software-development life-cycles and phases. However, it is unclear how difficult the implementation of the approach would be, and how accurate the predictions would be. Further research will help answer these questions.

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.

In the software reliability domain, the research about the modeling of software reliability is one of the earliest aspects. In this paper, a prediction model of software reliability based on the modular is presente. We apply Markov analysis theory to the software reliability prediction and discuss one kind software reliability model based on the module reliability and the module importance, in order to achieve the requirement of improving software quality. The proposed model utilizes the reliability data collected from software development projects and analyzes the module reliability and the module importance separately to study the whole software reliability of the system. Through application of this model to the actual project, we find that the new model has a better prediction precision and achieve ideal results.

Accurate reliability estimates can be obtained by using software reliability models only in the later phase of software testing. However, for cost effective and timely, management prediction in the early phase is important. Non-homogenerous Poisson process (NHPP) models and Artificial Neural Network (ANN) models are the most important Analytical software reliability growth models. In this paper we study an approach to using past fault-related data with Wavelet Networks model to improve reliability predictions in the early testing phase. A numerical example is shown with both actual and simulated datasets. The analysis with example shows that the proposed approach works effectively in the early phase of software testing.

Management technologies for improving software reliability are very important for software TQM (Total Quality Management). The quality characteristics of software reliability is that computer systems can continue to operate regularly without the occurrence of failures on software systems. In this chapter, we describe several recent developments in software reliability modeling and its applications as quantitative techniques for software quality/reliability measurement and assessment. That is, a quality engineering analysis of human factors affecting software reliability during the design-review phase, which is the upper stream of software development, and software reliability growth models based on stochastic differential equations and discrete calculus during the testing phase, which is the lower one, are discussed. And, we discuss quality-oriented software management analysis by applying the multivariate analysis method and the existing software reliability growth models to actual process monitoring data. Finally, we investigate an operational performability evaluation model for the software-based system, introducing the concept of systemability which is defined as the reliability characteristic subject to the uncertainty of the field environment.KeywordsHuman factor analysisDesign-review experimentOSS reliabilityStochastic differential equationDiscrete modelingDifference equationSoftware managementSoftware project assessmentSoftware performability modelingSystemability assessment

SREPT: software reliability estimation and prediction tool