An empirical study of program quality during testing and maintenance

In this paper, we report the results of a study conducted on a large commercial software system written in assembly language. Unlike studies of the past, our data represent the unit test, integration, and all categories of the maintenance phase: adaptive, perfective, and corrective. The results confirm that faults and change activity are related to software measurements. In addition, we report the relationship between the number of design change requests and software measurements. This new observation has the potential to aid the software engineering management process. Finally, we demonstrate the value of multiple regression models over simple regression models.     

Investigating ARIMA models of software system quality

In this paper, we investigate how to incorporate program complexity measures with a software quality model. We collect software complexity metrics and fault counts from each build during the testing phase of a large commercial software system. Though the data are limited in quantity, we are able to predict the number of faults in the next build. The technique we used is called times series analysis and forecasting. The methodology assumes that future predictions are based on the history of past observations. We will show that the combined complexity quality model is an improvement over the simpler quality only model. Finally, we explore how the testing process used in this development may be improved by using these predictions and suggest areas for future research.     

A method for diagnosis of current faults in antenna arrays using neural networks

A method for detection of faulty elements in antenna arrays from far‐field radiation pattern is presented. The proposed technique finds variation of current from correct values in the faulty elements. A step wise approach is proposed to determine magnitude and phase of current excitation and location of faulty element using neural networks. The results with radial basis function neural network and probabilistic neural network are compared. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

广义回归神经网络在软件质量预测中的应用

软件质量预测技术是软件质量评价体系中的关键技术,它能够对用户所关心的软件质量特性进行评价。广义回归神经网络在逼近能力、分类能力和学习速度方面具有较强优势。采用基于软件度量的广义回归神经网络构造质量预测模型能够从历史数据中寻找软件度量之间的相关关系。对软件缺陷数进行预测的实验说明了模型的有效性、精确性,实验结果令人满意。     

Model predictive control of an industrial packed bed reactor using neural networks

This paper discusses an industrial application of a multivariable nonlinear feedforward/feedback model predictive control where the model is given by a dynamic neural network. A multi-pass packed bed reactor temperature profile is modelled via recurrent neural networks using the backpropagation through time training algorithm. This model is then used in conjunction with an optimizer to build a nonlinear model predictive controller. Results show that, compared with conventional control schemes, the neural network model based controller can achieve tighter temperature control for disturbance rejection     

Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques

High-assurance and complex mission-critical software systems are heavily dependent on reliability of their underlying software applications. An early software fault prediction is a proven technique in achieving high software reliability. Prediction models based on software metrics can predict number of faults in software modules. Timely predictions of such models can be used to direct cost-effective quality enhancement efforts to modules that are likely to have a high number of faults. We evaluate the predictive performance of six commonly used fault prediction techniques: CART-LS (least squares), CART-LAD (least absolute deviation), S-PLUS, multiple linear regression, artificial neural networks, and case-based reasoning. The case study consists of software metrics collected over four releases of a very large telecommunications system. Performance metrics, average absolute and average relative errors, are utilized to gauge the accuracy of different prediction models. Models were built using both, original software metrics (RAW) and their principle components (PCA). Two-way ANOVA randomized-complete block design models with two blocking variables are designed with average absolute and average relative errors as response variables. System release and the model type (RAW or PCA) form the blocking variables and the prediction technique is treated as a factor. Using multiple-pairwise comparisons, the performance order of prediction models is determined. We observe that for both average absolute and average relative errors, the CART-LAD model performs the best while the S-PLUS model is ranked sixth.     

An investigation of artificial neural networks based prediction systems in software project management

A critical issue in software project management is the accurate estimation of size, effort, resources, cost, and time spent in the development process. Underestimates may lead to time pressures that may compromise full functional development and the software testing process. Likewise, overestimates can result in noncompetitive budgets. In this paper, artificial neural network and stepwise regression based predictive models are investigated, aiming at offering alternative methods for those who do not believe in estimation models. The results presented in this paper compare the performance of both methods and indicate that these techniques are competitive with the APF, SLIM, and COCOMO methods.     

具有Hammerstein模型描述的非线性系统的基于混合神经网络的预测控制

本文针对多输入多输出Hamrnerstein模犁提出了一种基于混合神经网络的模犁预测控制策略,控制器采用线性优化机构和高斯径向基神经网络串联.该策略不需要假设Hammerstein模型的非线性部分由多项式构成,避免了已有研究在无根或重根情况下存在导致预测控制的优化特征丧失问题,而采用混合神经网络则避免了采用传统神经网络拟合动态映射时存在的网络规模大和实时性差的不足.     

Selection of smoothing parameter estimators for general regression neural networks – Applications to hydrological and water resources modelling

Multi-layer perceptron artificial neural networks are used extensively in hydrological and water resources modelling. However, a significant limitation with their application is that it is difficult to determine the optimal model structure. General regression neural networks (GRNNs) overcome this limitation, as their model structure is fixed. However, there has been limited investigation into the best way to estimate the parameters of GRNNs within water resources applications. In order to address this shortcoming, the performance of nine different estimation methods for the GRNN smoothing parameter is assessed in terms of accuracy and computational efficiency for a number of synthetic and measured data sets with distinct properties. Of these methods, five are based on bandwidth estimators used in kernel density estimation, and four are based on single and multivariable calibration strategies. In total, 5674 GRNN models are developed and preliminary guidelines for the selection of GRNN parameter estimation methods are provided and tested.     

A fast procedure for the training of neural networks

A new training paradigm for artificial neural networks is described. The technique utilizes a polynomial approximation to the sigmoidal processing function and directly integrates principal components analysis (PCA) into the network training philosophy. A major benefit of the new technique is that off-line network training is ‘one-shot’, contrary to the standard iterative techniques available in the literature. Further training may be performed on-line in a recursive fashion, yielding an adaptive neural network. Additionally, the new philosophy incorporates a systematic procedure for determining the number of neurons in the hidden layer of the network. The training procedure is first described and the implications of the training philosophy discussed. Some results, including applications to industrial chemical processes, are then presented to highlight the power of the technique. The systems considered are a continuous stirred tank reactor and a polymerization reactor.     

Computational neural networks for mapping calorimetric data: Application of feed-forward neural networks to kinetic parameters determination and signals filtering

Feedforward neural networks have been used for kinetic parameters determination and signal filtering in differential scanning calorimetry. The proper learning function was chosen and the network topology was optimised using an empiric procedure. The learning process was achieved using various simulated thermoanalytical curves computed for several thermodynamic and kinetic parameters. Various amounts of simulated noise were added on the power signals. The resilient-propagation algorithm led to the best minimisation of the error computed over all the patterns. Relative errors on the thermodynamic and kinetic parameters were evaluated and compared to those obtained with the usual thermal analysis methods. The results obtained are very promising, and the errors are much lower than with usual methods, especially in the presence of noisy signals. This study shows that simulated thermoanalytical curves produced by Joule effect may be used for the deconvolution of the response of the apparatus, by using artificial neural networks.     

人工神经网络在预测服装企业安全库存的应用

安全库存是一种额外持有的库存，它作为一种缓冲器用来补偿在订货提前期内实际需求超过期望需求量或实际提前期超过期望提前期所产生的需求。在服装企业中一般凭经验来设定安全库存，但实际效果不佳，应用人工神经网络，建立BP神经网络模型，用多个影响安全库存的指标及安全库存对网络进行训练，以达到对安全库存量预测的目的，经验证和预测效果十分理想。     

Digitizing uncertainty modeling for reverse engineering applications: regression versus neural networks

The coordinate measuring machine is one of the two types of digitizers most popularly used in reverse engineering. A number of factors affect the digitizing uncertainty, such as travel speeds of the probe, pitch values (sampling points), probe angles (part orientations), probe sizes, and feature sizes. A proper selection of these parameters in a digitization or automatic inspection process can improve the digitizing accuracy for a given coordinate-measuring machine. To do so, some empirical models or decision rules are required. This paper applies and compares the nonlinear regression analysis and neural network modeling methods in developing empirical models for estimating the digitizing uncertainty. The models developed in this research can aid error prediction, accuracy improvement, and operation parameter selection in computer-aided reverse engineering and automatic inspection.     

Comparative analysis of statistical and machine learning methods for predicting faulty modules

The demand for development of good quality software has seen rapid growth in the last few years. This is leading to increase in the use of the machine learning methods for analyzing and assessing public domain data sets. These methods can be used in developing models for estimating software quality attributes such as fault proneness, maintenance effort, testing effort. Software fault prediction in the early phases of software development can help and guide software practitioners to focus the available testing resources on the weaker areas during the software development. This paper analyses and compares the statistical and six machine learning methods for fault prediction. These methods (Decision Tree, Artificial Neural Network, Cascade Correlation Network, Support Vector Machine, Group Method of Data Handling Method, and Gene Expression Programming) are empirically validated to find the relationship between the static code metrics and the fault proneness of a module. In order to assess and compare the models predicted using the regression and the machine learning methods we used two publicly available data sets AR1 and AR6. We compared the predictive capability of the models using the Area Under the Curve (measured from the Receiver Operating Characteristic (ROC) analysis). The study confirms the predictive capability of the machine learning methods for software fault prediction. The results show that the Area Under the Curve of model predicted using the Decision Tree method is 0.8 and 0.9 (for AR1 and AR6 data sets, respectively) and is a better model than the model predicted using the logistic regression and other machine learning methods.     

Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

Abstract: We aimed to examine the diagnostic performances of multilayer perceptron neural networks (MLPNNs) for predicting coronary artery disease and to compare them with different types of artificial neural network methods, namely recurrent neural networks (RNNs) and two statistical methods (quadratic discriminant analysis (QDA) and logistic regression (LR)). MLPNNs were trained with backpropagation, quick propagation, delta-bar-delta and extended delta-bar-delta algorithms as classifiers; the RNN was trained with the Levenberg–Marquardt algorithm; LR and QDA were used for predicting coronary artery disease. Coronary artery disease was classified with accuracy rates varying from 79.9% to 83.9% by MLPNNs. Even though MLPNNs achieved higher accuracy rates than the statistical methods, LR (73.2%) and QDA (58.4%), their performances were lower compared to the RNN (84.7%). Among the four different types of training algorithms that trained MLPNNs, quick propagation achieved the highest accuracy rate; however, it was lower than the RNN trained with the Levenberg–Marquardt algorithm. RNNs, which demonstrated 84.7% accuracy and 86.5% positive predictive rates, may be a helpful tool in medical decision making for diagnosis of coronary artery disease.     

模糊神经网络在移动机器人信息融合中的应用

针对移动机器人所用的传感器,提出了一种用于多传感器信息融合的方法,将模糊逻辑和神经网络结合起来,构建了模糊神经网络,并建立了网络的计算模型.通过建立的模糊神经网络对移动机器人的多传感器信息进行融合,实现了移动机器人对动态环境中障碍和环境类型的实时识别以及无冲突运动.网络的训练和试验表明该方法在移动机器人躲避运动物体中是可行的.     

神经网络预测控制局部优化初值确定方法

为解决局部优化算法初值选取不当造成神经网络预测控制性能下降的问题,本文提出了一种动态确定初值的方法.在每次优化时通过逆网络将初值选在输出误差最小点,通过修正目标性能函数中的权重因子来确保初值与当前控制量之间存在极值,并在理论上进行了证明.以BP神经网络预测控制为例,采用牛顿拉夫逊算法实现滚动优化,对所提方法进行了仿真实验,结果表明能够解决初值问题,提高控制系统的可靠性.     

The essential order of approximation for neural networks

There have been various studies on approximation ability of feedforward neural networks (FNNs). Most of the existing studies are, however, only concerned with density or upper bound estimation on how a multivariate function can be approximated by an FNN, and consequently, the essential approximation ability of an FNN cannot be revealed. In this paper, by establishing both upper and lower bound estimations on approximation order, the essential approximation ability (namely, the essential approximation order) of a class of FNNs is clarified in terms of the modulus of smoothness of functions to be approximated. The involved FNNs can not only approximate any continuous or integrable functions defined on a compact set arbitrarily well, but also provide an explicit lower bound on the number of hidden units required. By making use of multivariate approximation tools, it is shown that when the functions to be approximated are Lipschitzian with order up to 2, the approximation speed of the FNNs is uniquely deter     

Ranking importance of input parameters of neural networks

Artificial neural networks have been used for simulation, modeling, and control purposes in many engineering applications as an alternative to conventional expert systems. Although neural networks usually do not reach the level of performance exhibited by expert systems, they do enjoy a tremendous advantage of very low construction costs. This paper addresses the issue of identifying important input parameters in building a multilayer, backpropagation network for a typical class of engineering problems. These problems are characterized by having a large number of input variables of varying degrees of importance; and identifying the important variables is a common issue since elimination of the unimportant inputs leads to a simplification of the problem and often a more accurate modeling or solution. We compare three different methods for ranking input importance: sensitivity analysis, fuzzy curves, and change of MSE (mean square error); and analyze their effectiveness. Simulation results based on experiments with simple mathematical functions as well as a real engineering problem are reported. Based on the analysis and our experience in building neural networks, we also propose a general methodology for building backpropagation networks for typical engineering applications.