Software Failure Data Research Articles

An S-shaped software reliability model with imperfect debugging and improved testing learning process

In this paper, we propose a non-homogeneous Poisson process (NHPP) based S-shaped software reliability growth model (SRGM) in presence of imperfect debugging with a new exponentially increasing fault content function and S-shaped fault detection rate. We develop the fault content function considering learning capability of testing team during software development process. Fault content increases rapidly at the beginning of testing process while it grows gradually at the end of testing process due to increasing efficiency of testing team with testing time. We use maximum likelihood estimation (MLE) method to estimate model parameters. Applicability of the proposed model has been presented by comparing with established models in terms of goodness of fit and predictive validity using two software failure data sets. Experimental results show that the proposed model gives better fit to real failure data sets and predicts future failure behaviour of software development accurately than established models.

Research on Software Reliability Assessment with Optimum Reserved Strategy Genetic Programming

The software failure data can be analyzed by Genetic Programming (GP for short). Meanwhile, the Particle Swarm Optimization (PSO for short) algorithm is taken to find the rational parameters during the dynamic modeling and finally the optimized model structure of software reliability can be obtained, all of which can make sure the accuracy of software reliability forecasting can be dramatically improved. This paper has also analyzed and validated Genetic Programming has the convergent characteristics, which make use of optimum reserved strategy. And the results testify that Genetic Programming can meet the best solution needs of the variety disciplinary of the failure behaviors, which can further testify Genetic Programming is able to apply for software system testing as well as guarantee the availability of the data. The application inconsistency of new model shows obviously comparing with other traditional models, which has much more theory research merit and practical application meaning to the analysis of software reliability models as well as the forecast of software failure behaviors.

Effect of change point and imperfect debugging in software reliability and its optimal release policy

This article presents a software reliability growth model based on non-homogeneous Poisson process. The main focus of this article is to deliver a method for software reliability modelling incorporating the concept of time-dependent fault introduction and fault removal rate with change point. Also in this article, a cost model with change point has been developed. Based on the cost model optimal release policy with change point has been discussed. Maximum likelihood technique has been applied to estimate the parameters of the model. The proposed model has been validated using some real software failure data. Comparison has been made with models incorporating change point and without change point. The application of the proposed cost model has been shown using some numerical examples.

Research on Software Reliability Based on Forecasting Model Combined by LS-SVM and Markov Chain

To improve the accuracy of prediction on software failure data, one combine forecasting model is proposed based on least square support vector machine (LS-SVM) and Markov chain. First, LS-SVM optimized by simulated annealing algorithm (SA) is adopted to establish the time series forecasting model on software failure data. Then, in order to improve the prediction accuracy, the prediction interval is narrowed by means of Markov chain. Finally, after applying the combination forecasting model to the predicting of one commercial software failure data, the results indicate that the model has a certain precision and reliability.

Software Reliability Prediction using Neural Network with Encoded Input

neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability. Software engineers can measure or forecast software reliability using software reliability models. In real software development process, cost and time are limited. The models are used to develop reliable software under given time and cost constraints. The software managers also determine the release time of the software with the help these models. In past few years, a large number of software reliability models/statistical models have been developed. Every model is based on certain assumptions. So the predictive capability of different models is different for different data sets. There exist no single models that can best suit for all cases. The statistical models are also influenced by different external parameters. To overcome these problems, non-parametric models like neural network and support vector machines have been used for last few years. The non-parametric models are not influenced by any external parameter and also not based on assumptions which are unrealistic in real situation. Neural network has become an alternative method in software reliability modeling, evaluation and prediction. In the literature, many neural network based software reliability prediction models exist and their prediction capability is proven better than some statistical models (4-6). All the neural network based software reliability models are basically experimental in nature. Anyone who wants to apply the models on real software data set, they have to first find out the architecture of the network in hit and trial method. In real cases, people lose their confidence to apply the models with high level of confidence. In this work, neural network models with Exponential and Logarithmic encoding scheme have been proposed to build non-parametric models for software reliability prediction. It has been shown in (14) that neural network models with the above two encoding scheme has better prediction for cumulative number of failures than some statistical models. But in (14), the value of the encoding parameter is determined by doing repeated experiments with hit and trial method. The purpose of this paper is to provide a guideline for the selection of encoding parameter which gives consistent results for different datasets. The proposed approach is applied using eighteen different data sets and found to provide consistent result for all datasets. The approach has been compared using three data sets with existing statistical models with change points. The rest of the paper has been organized as follows: In Section 2, some works related to software reliability prediction are introduced. Section 3 presents the proposed method to predict cumulative number of failures in software using neural network with two different encoding schemes such as Exponential and Logarithmic encoding. Section 4 shows the experimental result. Finally, Section 5 concludes the paper.

Assessing Reliable Software using SPRT based on LPETM

Software reliability assessment is increasingly important in developing and testing new software products. Logarithmic Poisson Execution Time Model (LPETM) is a software reliability model which predicts the expected failures and hence related reliability quantities better than existing software reliability models. It uses Non-Homogeneous Poisson Process(NHPP) with a mean value function that is dependent on exponentially falling fault detection rate. The well known sequential Probability Ratio Test(SPRT) procedure of statistical science is adopted for this model in order to decide upon the reliability / unreliability of developed software. The model is evaluated by using 6 Data Sets. General Terms Decision Rule , Software testing, Software failure data,Quality Software. .

Software reliability growth models with normal failure time distributions

This paper proposes software reliability growth models (SRGM) where the software failure time follows a normal distribution. The proposed model is mathematically tractable and has sufficient ability of fitting to the software failure data. In particular, we consider the parameter estimation algorithm for the SRGM with normal distribution. The developed algorithm is based on an EM (expectation-maximization) algorithm and is quite simple for implementation as software application. Numerical experiment is devoted to investigating the fitting ability of the SRGMs with normal distribution through 16 types of failure time data collected in real software projects.

A Bayesian hidden Markov model for imperfect debugging

In this paper we present a new model to describe software failures from a debugging process. Our model allows for the imperfect debugging scenario by considering potential introduction of new bugs to the software during the development phase. Since the introduction of bugs is an unobservable process, latent variables are introduced to incorporate this property via a hidden Markov model. We develop a Bayesian analysis of the model and discuss its extensions. We also consider how to infer the unknown number of states of the hidden Markov model. The model and the Bayesian analysis are implemented to actual software failure data.

Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.

Application of Fuzzy Time Series in Prediction of Time Between Failures & Faults in Software Reliability Assessment

Since last seventies, various software reliability growth models (SRGMs) have been developed to estimate different measures related to quality of software like: number of remaining faults, software failure rate, reliability, cost, release time, etc. Most of the exiting SRGMs are probabilistic. These models have been developed based on various assumptions. The entire software development process is performed by human being. Also, a software can be executed in different environments. As human behavior is fuzzy and the environment is changing, the concept of fuzzy set theory is applicable in developing software reliability models. In this paper, two fuzzy time series based software reliability models have been proposed. The first one predicts the time between failures (TBFs) of software and the second one predicts the number of errors present in software. Both the models have been developed considering the software failure data as linguistic variable. Usefulness of the models has been demonstrated using real failure data.

Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm

Software reliability prediction is very important for minimising cost and improving the effectiveness of the software development process. As an important method, relative data during software lifecycle is used to analyse and predict software reliability. However, predicting the variability of software reliability with time is very difficult. Recently, support vector regression (SVR) has been widely applied to solve non-linear predicting problems in many fields such as software reliability prediction and has obtained well performance in many situations, and it is still difficult to select its parameters. Previously, intelligence optimisation algorithms, such as genetic algorithm (GA), are mostly used for finding better parameters of SVR, however existing methods of selecting parameters require usually has some disadvantages. In this study, to overcome weaknesses of GA, such as the local minima and the premature convergence problems, GA and simulated annealing (SA) are integrated into a new optimise algorithm, called GA-SA, it is then applied to SVR for predicting software reliability. The authors compare proposed GA-SA-SVR model with other software reliability models through real software failure data. The experimental results show that the proposed GA-SA-SVR model can obtain better predictions results than the other models and has a fairly accurate prediction capability.

Estimation and Analysis of Some Generalized Multiple Change-Point Software Reliability Models

Software typically undergoes debugging during both a testing phase before product release, and an operational phase after product release. But it is noted that the fault detection and removal processes during software development and operation are different. For example, the fault removal during operation occurs generally at a slower pace than development. In this paper, we derive a powerful, easily deployable technique for software reliability prediction and assessment in the testing and operational phases. We first review how several existing software reliability growth models (SRGM) based on non- homogeneous Poisson processes (NHPP) can be readily derived from a unified theory. With the unified theory, we further incorporate the concept of multiple change-points, i.e. points in time when the software environment changes, into software reliability modeling. Several models are proposed and discussed under both ideal and imperfect debugging conditions. We estimate the parameters of the proposed models by employing real software failure data, and give a fair comparison with some existing SRGM. Numerical results show that the proposed models can provide good software reliability prediction in the various stages of software development and operation. Our approach is flexible; we can model various environments ranging from exponential-type to S-shaped NHPP models.

Autonomous and adaptive procedure for cumulative failure prediction

An autonomous adaptive reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available failure time data, Fuzzy Min–Max algorithm is used to globally optimize the number of the k Gaussian nodes. This technique allows determining and initializing the k-centers of the neural network architecture in an iterative way. The user does not have to define arbitrary some parameters. The optimized neural network architecture is then iteratively and dynamically reconfigured as new failure occurs. The performance of the proposed approach has been tested using sixteen real-time software failure data.

Risk-based software release policy under parameter uncertainty

The determination of the optimal release time is a significant problem in the software development process. Most existing research on this problem is based on the assumption that the model parameters are either known or can be accurately estimated. Due to the uncertainties associated with parameter estimation created by the very limited amount of software failure data that is generally available, the accuracy of the optimum release time determined by traditional approaches is questionable. When the mean value of the optimal release time is used, for example, there is only a 50 per cent chance that the reliability target is met at the time of release. In this paper, an optimal software release policy under parameter uncertainty is studied. To take parameter uncertainty into consideration, an optimal risk-based software release time determination approach is introduced. Application examples are given to illustrate this approach and simulation studies are carried out. The presented results can help management to consider multiple risk levels in order to reach a more reasonable decision.

Best Subset Selection of ARMA and ARIMA Models for Software Reliability Estimation

Many software reliability models have been proposed during last three decades. Almost all these software reliability models have been developed based on many assumptions. One approach for development of assumption free software reliability model is time series. In this paper ARMA and ARIMA models have been developed for predicting software reliability. The proposed models have been validated using some real software failure data.

DETECTION OF RELIABLE SOFTWARE USING SPRT

In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper, we have proposed the performance of SPRT on Time domain data using exponential imperfect debugging model and analyzed the results by applying on 5 data sets. The parameters are estimated by using Maximum Likelihood Estimation. Wald's procedure is particularly relevant if the data is collected sequentially. Sequential Analysis is different from that of Classical Hypothesis Testing where the number of cases tested or collected, is fixed at the beginning of the experiment. In Classical Hypothesis Testing, the data collection is executed without analysis and consideration of the data. After all the data is collected the analysis is done, conclusions are drawn. However, in Sequential Analysis every case is analyzed directly after being collected, the data collected up to that moment is then compared with certain threshold values, incorporating the new information obtained from the freshly collected case. This approach allows one to draw conclusions during the data collection, and a final conclusion can possibly be reached at a much earlier stage as is the case in Classical Hypothesis Testing. The advantages of Sequential Analysis is easily seen. As data collection can be terminated after fewer cases and decisions taken earlier, the savings in terms of human life and misery, and financial savings, might be considerable. In the analysis of software failure data, we often deal with either Time Between Failures or failure count in a given time interval. If it is further assumed that the average number of recorded failures in a given time interval is directly proportional to the length of the interval and the random number of failure occurrences in the interval is explained by a Poisson process, then we know that the probability equation of the stochastic process representing the failure occurrences is given by a homogeneous poisson process with the expression     ! n

A New Software Reliability Model Considering Warps Between Different Software Failure Processes

The software reliability model is one of the important approaches to predict and evaluate software reliability quantitatively.The software failure data to be analyzed should be considered as a particular realization of a stochastic process.In this paper,a new software reliability growth model considering warps between different software failure processes is proposed.The experimental results based on two real data sets show that the proposed model has better prediction and curve fitting abilities than that of some other conditional NHPP software reliability growth models.The measures used for comparison are mean square of fitting error,predicted error and relative error.

Failure model of software reliability based on LSSVRM and simulated annealing algorithm

According to the characteristics of the software failure data,the Least Square Support Vector Regression Machine (LSSVRM) was applied to build the software failure model.The LSSVRM was used to construct a fitting model according to the failure data that is a small sample and the parameters in LSSVRM were optimized by the simulated annealing algorithm to get the SA-LSSVRM and further improve the fitting model.Compared with the failure mode of NonHomogeneous Poisson Process (NHPP),the failure model based on the LSSVRM and SA algorithm has the higher fitting precision and the adjustments of the model parameters could be simplified.

NHPP Software Reliability Growth Model Considering Imperfect Debugging

PDF HTML阅读 XML下载 导出引用 引用提醒 考虑不完美排错情况的NHPP 类软件可靠性增长模型 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the Science Foundation of Aeronautics of China under Grant No.20095558008 (航空科学基金) NHPP Software Reliability Growth Model Considering Imperfect Debugging Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:针对现有NHPP 类软件可靠性增长模型对故障排错过程中不完美排错情况考虑不完全的现状,提出了一 种新的软件可靠性增长模型.该模型全面考虑了不完美排错的两种情况:既考虑了排错过程中引入新错误的可能性, 又考虑了不完全排错的情况,并且引入了一种故障排除率随时间变化的故障排除率函数,使模型更符合实际情况.利 用公开发表的两组不同的软件失效数据对该模型进行验证的结果表明,与现有的对不完美排错情况考虑不完全的 模型相比,该模型能够取得更好的拟合结果和预测效果. Abstract:Addressing at the problem that the considerations of imperfect debugging phenomenon in the existing software reliability growth model are limited, this paper proposes a software reliability growth model which takes into account of the imperfect debugging comprehensively. Both the fault introduction and the fault removal efficiency are considered in this model, and a time-dependent fault removal efficiency function is introduced. The goodness-of-fit and the predictive power of the new model are examined by using a public software failure data set. The results show that compared with other existing models, the proposed model fits the failure data better and can predict this set of data more accurately. 参考文献 相似文献 引证文献

A Software Reliability Growth Model for Three-Tier Client Server System

With the ever-increasing role that software is playing in our reallife systems, concern has steadily grown over the quality of the software products. In today’s life the computers are being used to monitor and control safety critical and civilian systems with a great demand for high-quality software products. So reliability is a primary concern for both software developers and software users. In literature many software reliability growth models have been proposed over the years to estimate and predict reliability of software products. But it is often very difficult for project managers and practitioners to determine which model is more useful in a particular domain and up to what extent. In this paper we propose a NHPP based software reliability growth model for three-tier client server systems. The present model composed of three layers of client-server architecture related to presentation logic, business logic and database stored at backend. Presentation layer contains forms or server pages which presents the user interface for the application, displays the data, collects the user inputs and sends the requests to next layer. Business layer, which provides the support services to receive the requests for data from user tier, evaluates against business rules, passes them to the data tier and incorporates the business rules for the application. Data layer includes data access logic, database driver(s), query engines used for communicating directly with the data store of a database. The model has been validated through standard dataset consists of software failure data on various projects released from the software reliability dataset and applying to a live commercial application.