Software reliability prediction from the telecommunications carrier perspective

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.

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.

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

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.

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.

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.

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

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.

In this thesis, we present methodologies involving a data structure called the debugging graph whereby the predictive performance of software reliability models can be analyzed and improved under laboratory conditions. This procedure substitutes the averages of large sample sets for the single point samples normally used as inputs to these models and thus supports scrutiny of their performances with less random input data.\nInitially, we describe the construction of an extensive database of empirical reliability data which we derived by testing each partially debugged version of subject software represented by complete or partial debugging graphs. We demonstrate how these data can be used to assign relative sizes to known bugs and to simulate multiple debugging sessions. We then present the results from a series of proof-of-concept experiments.\nWe show that controlling fault recovery order as represented by the data input to some well-known reliability models can enable them to produce more accurate predictions and can mitigate anomalous effects we attribute to manifestations of the fault interaction phenomenon. Since limited testing resources are common in the real world, we demonstrate the use of two approximation techniques, the surrogate oracle and path truncations, to render the application of our methodologies computationally feasible outside a laboratory setting. We report results which support the assertion that reliability data collected from just a partial debugging graph and subject to these approximations qualitatively agrees with those collected under ideal laboratory conditions, provided one accounts for optimistic bias introduced by the surrogate in later prediction stages. We outline an algorithmic approach for using data derived from a partial debugging graph to improve software reliability predictions, and show its complexity to be no worse than O(n2). We summarize some outstanding questions as areas for future investigations of and improvements to the software reliability prediction process.

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.

This paper presents a Bayes nonparametric approach for tracking and predicting software reliability. We use the common assumptions on the software operational environment to get a stochastic model where the successive times between software failures are exponentially distributed; their failure rates have Markov priors. Under these general assumptions we give Bayes estimates of the parameters that assess and predict the software reliability. We give algorithms (based on Monte-Carlo methods) to compute these Bayes estimates. Our approach allows the reliability analyst to construct a personal software reliability model simply by specifying the available prior knowledge; afterwards the results in this paper can be used to get Bayes estimates of the useful reliability parameters. Examples of possible prior physical knowledge concerning the software testing and correction environments are given. The maximum-entropy principle is used to translate this knowledge to prior distributions on the failure-rate process. Our approach is used to study some simulated and real failure data sets.

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 & Prediction of Software Reliability: ANN Approach