Software development cost estimation using wavelet neural networks

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost and quality of software product under development. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper, we propose the use of wavelet neural network (WNN) to forecast the software development effort. We used two types of WNN with Morlet function and Gaussian function as transfer function and also proposed threshold acceptance training algorithm for wavelet neural network (TAWNN). The effectiveness of the WNN variants is compared with other techniques such as multilayer perceptron (MLP), radial basis function network (RBFN), multiple linear regression (MLR), dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector machine (SVM) in terms of the error measure which is mean magnitude relative error (MMRE) obtained on Canadian financial (CF) dataset and IBM data processing services (IBMDPS) dataset. Based on the experiments conducted, it is observed that the WNN-Morlet for CF dataset and WNN-Gaussian for IBMDPS outperformed all the other techniques. Also, TAWNN outperformed all other techniques except WNN.     

基于RBF神经网络的软件成本估算模型

软件成本估算是软件开发过程中一项非常重要的活动,但现有的方法在准确估算软件成本方面还存在不足。针对软件成本估算不够准确的现状,提出了一种基于RBF神经网络的软件成本估算模型。该模型采用样本聚类的方法确定隐含层节点数,利用遗传算法对隐层节点中心值和高斯函数的宽度进行优化,利用线性最小二乘法训练网络的权值。实验证明,该模型能够准确有效地估算软件成本。     

Software project development cost estimation

This paper reports the results of an empirical investigation of the relationships between effort expended, time scales, and project size for software project development. The observed relationships were compared with those predicted by Lawrence Putnam's Rayleigh curve model and Barry Boehm's COCOMO model. The results suggested that although the form of the basic empirical relationships were consistent with the cost models, the COCOMO model was a poor estimator of cost for the current data set and the data did not follow the Rayleigh curve suggested by Putnam. However, the results did suggest that it was possible to develop cost models tailored to a particular environment and to improve the precision of the models as they are used during the development cycle by including additional information such as the known effort for the early development phases. The paper finishes by discussing some of the problems involved in developing useful cost models.     

Software development cost estimation using function points

This paper presents an assessment of several published statistical regression models that relate software development effort to software size measured in function points. The principal concern with published models has to do with the number of observations upon which the models were based and inattention to the assumptions inherent in regression analysis. The research describes appropriate statistical procedures in the context of a case study based on function point data for 104 software development projects and discusses limitations of the resulting model in estimating development effort. The paper also focuses on a problem with the current method for measuring function points that constrains the effective use of function points in regression models and suggests a modification to the approach that should enhance the accuracy of prediction models based on function points in the future     

Integrating risk assessment with cost estimation

Incorporating hard data into risk estimates can help make them more accurate. The author developed a quantitative method and a corresponding tool that draw on questionnaires and project history to help calculate software project risk contingencies: RiskMethod and RiskTool     

Software cost estimation with incomplete data

The construction of software cost estimation models remains an active topic of research. The basic premise of cost modeling is that a historical database of software project cost data can be used to develop a quantitative model to predict the cost of future projects. One of the difficulties faced by workers in this area is that many of these historical databases contain substantial amounts of missing data. Thus far, the common practice has been to ignore observations with missing data. In principle, such a practice can lead to gross biases and may be detrimental to the accuracy of cost estimation models. We describe an extensive simulation where we evaluate different techniques for dealing with missing data in the context of software cost modeling. Three techniques are evaluated: listwise deletion, mean imputation, and eight different types of hot-deck imputation. Our results indicate that all the missing data techniques perform well with small biases and high precision. This suggests that the simplest technique, listwise deletion, is a reasonable choice. However, this will not necessarily provide the best performance. Consistent best performance (minimal bias and highest precision) can be obtained by using hot-deck imputation with Euclidean distance and a z-score standardization     

Software cost estimation: Present and future

The state-of-the-art in software cost estimation is reviewed. The estimated cost of a software system varies widely with the model used. Some variation in cost estimation is attributable to the anomolies in the cost data base used in developing the model. The other variations, it is claimed are due to the presence or absence of certain ‘qualities’ in the final product. These qualities are measures of ‘goodness’ in design, development, and test-integration phases of software. To consider quality as a driver of software cost, we have suggested an association between cost and quality and have proposed a way to use quality metrics to estimate software cost.     

Research of neural network algorithm based on factor analysis and cluster analysis

Aiming at the large sample with high feature dimension, this paper proposes a back-propagation (BP) neural network algorithm based on factor analysis (FA) and cluster analysis (CA), which is combined with the principles of FA and CA, and the architecture of BP neural network. The new algorithm reduces the feature dimensionality of the initial data through FA to simplify the network architecture; then divides the samples into different sub-categories through CA, trains the network so as to improve the adaptability of the network. In application, it is first to classify the new samples, then using the corresponding network to predict. By an experiment, the new algorithm is significantly improved at the aspect of its prediction precision. In order to test and verify the validity of the new algorithm, we compare it with BP algorithms based on FA and CA.     

PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding

To simplify complicated traditional cost estimation flow, this study emphasizes the cost estimation approach for plastic injection products and molds. It is expected designers and R&D specialists can consider the competitiveness of product cost in the early stage of product design to reduce product development time and cost resulting from repetitive modification. Therefore, the proposed cost estimation approach combines factor analysis (FA), particle swarm optimization (PSO) and artificial neural network with two back-propagation networks, called FAPSO-TBP. In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established. To verify the proposed FAPSO-TBP approach, comparisons with the FAPSO-SBP and general back-propagation artificial neural network (GBP) are made. The computational results show the proposed FAPSO-TBP approach is very competitive for the product and mold cost estimation problems of plastic injection molding.     

Behavior analysis of neural network ensemble algorithm on a virtual machine cluster

Ensemble learning has gained considerable attention in different learning tasks including regression, classification, and clustering problems. One of the drawbacks of the ensemble is the high computational cost of training stages. Resampling local negative correlation (RLNC) is a technique that combines two well-known methods to generate ensemble diversity—resampling and error negative correlation—and a fine-grain parallel approach that allows us to achieve a satisfactory balance between accuracy and efficiency. In this paper, we introduce a structure of the virtual machine aimed to test diverse selection strategies of parameters in neural ensemble designs, such as RLNC. We assess the parallel performance of this approach on a virtual machine cluster based on the full virtualization paradigm, using speedup and efficiency as performance metrics, for different numbers of processors and training data sizes.     

Volterra series analysis and synthesis of a neural network forvelocity estimation

The motion detection problem occurs frequently in many applications connected with computer vision. Researchers have studied motion detection based on naturally occurring biological circuits for over a century. In this paper, we propose and analyze a motion detection circuit which is based on nerve membrane conduction. It consists of two unidirectional neural networks connected in an opposing fashion. Volterra input-output (I-O) models are then derived for the network so that velocity estimation can be cast as a parameter estimation problem. The technique is demonstrated through simulation.     

MiNT-OLAP cluster: minimizing network transmission cost in OLAP cluster for main memory analytical database

Powerful storage, high performance and scalability are the most important issues for analytical databases. These three factors interact with each other, for example, powerful storage needs less scalability but higher performance, high performance means less consumption of indexes and other materializations for storage and fewer processing nodes, larger scale relieves stress on powerful storage and the high performance processing engine. Some analytical databases (ParAccel, Teradata) bind their performance with advanced hardware supports, some (Asterdata, Greenplum) rely on the high scalability framework of MapReduce, some (MonetDB, Sybase IQ, Vertica) highlight performance on processing engine and storage engine. All these approaches can be integrated into an storage-performance-scalability (S-P-S) model, and future large scale analytical processing can be built on moderate clusters to minimize expensive hardware dependency. The most important thing is a simple software framework is fundamental to maintain pace with the development of hardware technologies. In this paper, we propose a schema-aware on-line analytical processing (OLAP) model with deep optimization from native features of the star or snowflake schema. The OLAP model divides the whole process into several stages, each stage pipes its output to the next stage, we minimize the size of output data in each stage, whether in central processing or clustered processing. We extend this mechanism to cluster processing using two major techniques, one is using NetMemory as a broadcasting protocol based dimension mirror synchronizing buffer, the other is predicate-vector based DDTA-OLAP cluster model which can minimize the data dependency of star-join using bitmap vectors. Our OLAP model aims to minimize network transmission cost (MiNT in short) for OLAP clusters and support a scalable but simple distributed storagemodel for large scale clustering processing. Finally, the experimental results show the speedup and scalability performance.     

Software development effort prediction of industrial projects applying a general regression neural network

An important factor for planning, budgeting and bidding a software project is prediction of the development effort required to complete it. This prediction can be obtained from models related to neural networks. The hypothesis of this research was the following: effort prediction accuracy of a general regression neural network (GRNN) model is statistically equal or better than that obtained by a statistical regression model, using data obtained from industrial environments. Each model was generated from a separate dataset obtained from the International Software Benchmarking Standards Group (ISBSG) software projects repository. Each of the two models was then validated using a new dataset from the same ISBSG repository. Results obtained from a variance analysis of accuracies of the models suggest that a GRNN could be an alternative for predicting development effort of software projects that have been developed in industrial environments.     

航空集群网络可靠性估计路由选择策略

针对未来航空集群网络中节点间通信可靠性保障问题,提出一种基于移动预测的链路可靠性估计路由选择策略。首先,使用基于地理位置的移动预测方法对邻居节点的位置进行准确预测。其次,根据预测结果对当前节点的通信范围进行分区,对不同邻居节点所在区域的链路可靠性分别估计得到相应的可靠性估计函数值。根据得到的可靠性函数值使用概率选择模型并结合跳数因子进行路由选择。最后,以优化链路状态路由协议（Optimized Link State Routing protocol,OLSR）为基础对路由选择策略细节进行描述,并仿真验证了提出的路由选择策略的有效性。实验结果表明,该策略在适用于航空集群网络的基础上,能有效提高网络中节点间通信的可靠性。     

一种基于模糊决策树的软件成本估计模型

提出一种基于模糊决策树的软件成本估计模型。该模型能够把决策树的归纳学习能力和模糊集合所具有的表达能力相结合,在软件成本估计过程中以模糊集的形式预测相对误差范围,并利用决策树的判定规则分析误差源。最后,通过软件项目历史数据验证该软件成本估计模型的有效性。     

Software defect prediction using cost-sensitive neural network

The software development life cycle generally includes analysis, design, implementation, test and release phases. The testing phase should be operated effectively in order to release bug-free software to end users. In the last two decades, academicians have taken an increasing interest in the software defect prediction problem, several machine learning techniques have been applied for more robust prediction. A different classification approach for this problem is proposed in this paper. A combination of traditional Artificial Neural Network (ANN) and the novel Artificial Bee Colony (ABC) algorithm are used in this study. Training the neural network is performed by ABC algorithm in order to find optimal weights. The False Positive Rate (FPR) and False Negative Rate (FNR) multiplied by parametric cost coefficients are the optimization task of the ABC algorithm. Software defect data in nature have a class imbalance because of the skewed distribution of defective and non-defective modules, so that conventional error functions of the neural network produce unbalanced FPR and FNR results. The proposed approach was applied to five publicly available datasets from the NASA Metrics Data Program repository. Accuracy, probability of detection, probability of false alarm, balance, Area Under Curve (AUC), and Normalized Expected Cost of Misclassification (NECM) are the main performance indicators of our classification approach. In order to prevent random results, the dataset was shuffled and the algorithm was executed 10 times with the use of n-fold cross-validation in each iteration. Our experimental results showed that a cost-sensitive neural network can be created successfully by using the ABC optimization algorithm for the purpose of software defect prediction.     

BP神经网络在预测物流成本中的应用

影响物流成本的因素过多且较复杂,采用简单的猜测试赋值有较大的主观性,因此物流成本预测问题是一个非常复杂的非线性问题.本文根据物流成本与其影响因素之间映射关系,建立了BP神经网络模型,将其应用于物流成本的预测.实验结果表明,该模型具有较高的精度.     

分段卷积神经网络在文本情感分析中的应用

文本情感分析是当前网络舆情分析、产品评价、数据挖掘等领域的重要任务。由于当前网络数据的急剧增长,依靠人工设计特征或者传统的自然语言处理语法分析工具等进行分析,不但准确率不高而且费时费力。而传统的卷积神经网络模型均未考虑句子的结构信息,并且在训练时很容易发生过拟合。针对这两方面的不足,使用基于深度学习的卷积神经网络模型分析文本的情感倾向,采用分段池化的策略将句子结构考虑进来,分段提取句子不同结构的主要特征;并且引入Dropout算法以避免模型的过拟合和提升泛化能力。实验结果表明,分段池化策略和Dropout算法均有助于提升模型的性能,所提方法在中文酒店评价数据集上达到了91%的分类准确率,在斯坦福英文情感树库数据集五分类任务上达到了45.9%的准确率,较基线模型都有显著的提升。