基于BBNs的软件残留缺陷预测模型

介绍了软件残留缺陷的重要性,简要阐述了目前对残留缺陷进行预测的一些模型,并指出了其中的问题及现有模型适应性不好的原因,提出了基于BBNs的软件残留缺陷数预测模型,给出了模型构建的具体过程。     

基于贝叶斯公式的软件缺陷影响因素分析

随着时软件缺陷重视程度的提高，人们提出了很多软件缺陷预测模型，但所有的模型都只停留在缺陷数预测的基础上，不能系统分析出导致预测结果的真正原因。而本文结合一个具体的软件缺陷预测模型。利用贝叶斯公式对导致结果发生的影响因素进行了分析。此方法不但能对现有开发项目的一些重要影响因素起到控制作用，还为今后的开发项目提供了一定的经验数据，预防同类错误的再次发生。     

A defect prediction method for software versioning

New methodologies and tools have gradually made the life cycle for software development more human-independent. Much of the research in this field focuses on defect reduction, defect identification and defect prediction. Defect prediction is a relatively new research area that involves using various methods from artificial intelligence to data mining. Identifying and locating defects in software projects is a difficult task. Measuring software in a continuous and disciplined manner provides many advantages such as the accurate estimation of project costs and schedules as well as improving product and process qualities. This study aims to propose a model to predict the number of defects in the new version of a software product with respect to the previous stable version. The new version may contain changes related to a new feature or a modification in the algorithm or bug fixes. Our proposed model aims to predict the new defects introduced into the new version by analyzing the types of changes in an objective and formal manner as well as considering the lines of code (LOC) change. Defect predictors are helpful tools for both project managers and developers. Accurate predictors may help reducing test times and guide developers towards implementing higher quality codes. Our proposed model can aid software engineers in determining the stability of software before it goes on production. Furthermore, such a model may provide useful insight for understanding the effects of a feature, bug fix or change in the process of defect detection.

Negative samples reduction in cross-company software defects prediction

ContextSoftware defect prediction has been widely studied based on various machine-learning algorithms. Previous studies usually focus on within-company defects prediction (WCDP), but lack of training data in the early stages of software testing limits the efficiency of WCDP in practice. Thus, recent research has largely examined the cross-company defects prediction (CCDP) as an alternative solution.ObjectiveHowever, the gap of different distributions between cross-company (CC) data and within-company (WC) data usually makes it difficult to build a high-quality CCDP model. In this paper, a novel algorithm named Double Transfer Boosting (DTB) is introduced to narrow this gap and improve the performance of CCDP by reducing negative samples in CC data.MethodThe proposed DTB model integrates two levels of data transfer: first, the data gravitation method reshapes the whole distribution of CC data to fit WC data. Second, the transfer boosting method employs a small ratio of labeled WC data to eliminate negative instances in CC data.ResultsThe empirical evaluation was conducted based on 15 publicly available datasets. CCDP experiment results indicated that the proposed model achieved better overall performance than compared CCDP models. DTB was also compared to WCDP in two different situations. Statistical analysis suggested that DTB performed significantly better than WCDP models trained by limited samples and produced comparable results to WCDP with sufficient training data.ConclusionsDTB reforms the distribution of CC data from different levels to improve the performance of CCDP, and experimental results and analysis demonstrate that it could be an effective model for early software defects detection.     

A fuzzy logic based approach for phase-wise software defects prediction using software metrics

ContextThe software defect prediction during software development has recently attracted the attention of many researchers. The software defect density indicator prediction in each phase of software development life cycle (SDLC) is desirable for developing a reliable software product. Software defect prediction at the end of testing phase may not be more beneficial because the changes need to be performed in the previous phases of SDLC may require huge amount of money and effort to be spent in order to achieve target software quality. Therefore, phase-wise software defect density indicator prediction model is of great importance.ObjectiveIn this paper, a fuzzy logic based phase-wise software defect prediction model is proposed using the top most reliability relevant metrics of the each phase of the SDLC.MethodIn the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using nine software metrics of these four phases. The defect density indicator metric predicted at the end of the each phase is also taken as an input to the next phase. Software metrics are assessed in linguistic terms and fuzzy inference system has been employed to develop the model.ResultsThe predictive accuracy of the proposed model is validated using twenty real software project data. Validation results are satisfactory. Measures based on the mean magnitude of relative error and balanced mean magnitude of relative error decrease significantly as the software project size increases.ConclusionIn this paper, a fuzzy logic based model is proposed for predicting software defect density indicator at each phase of the SDLC. The predicted defects of twenty different software projects are found very near to the actual defects detected during testing. The predicted defect density indicators are very helpful to analyze the defect severity in different artifacts of SDLC of a software project.     

基于缺陷分层与PSO算法的软件缺陷预测模型

根据软件开发分层的思想,提出了基于软件缺陷分层的测试构架。在缺陷分层的测试架构下可以知道测试类之间的的关系和属性,容易发现关联缺陷。本文是在软件缺陷分层测试架构下结合粒子群优化(PSO)算法建立软件缺陷预测模型,并通过模拟实验验证预测模型的性能。结果显示该模型能有效提高预测缺陷效率和缺陷发生位置。     

基于受控马尔科夫链的软件缺陷优化测试策略

目前许多文献都讨论的受控马尔科夫链软件测试模型,是通过对部分假设条件进行特殊化处理后得到的,这将导致模型的适用范围较小且偏离实际应用。依据软件控制论思想,通过一系列新的制约条件的转换,提出一种改善的、测试资源约束下的受控马尔科夫链模型来消除已有模型的缺陷。同时,该模型能够在高效性、复杂性和适用性3方面达到一个平衡点。为了证明其有效,根据该模型设计了一种新的软件缺陷优化测试策略,并对该策略进行了仿真实验,将其与传统的随机测试策略进行了比较。实验结果表明,该模型具有较高的实用性和有效性。     

Estimating software readiness using predictive models

In this study, defect tracking is used as a proxy method to predict software readiness. The number of remaining defects in an application under development is one of the most important factors that allow one to decide if a piece of software is ready to be released. By comparing predicted number of faults and number of faults discovered in testing, software manager can decide whether the software is likely ready to be released or not.The predictive model developed in this research can predict: (i) the number of faults (defects) likely to exist, (ii) the estimated number of code changes required to correct a fault and (iii) the estimated amount of time (in minutes) needed to make the changes in respective classes of the application. The model uses product metrics as independent variables to do predictions. These metrics are selected depending on the nature of source code with regards to architecture layers, types of faults and contribution factors of these metrics. The use of neural network model with genetic training strategy is introduced to improve prediction results for estimating software readiness in this study. This genetic-net combines a genetic algorithm with a statistical estimator to produce a model which also shows the usefulness of inputs.The model is divided into three parts: (1) prediction model for presentation logic tier (2) prediction model for business tier and (3) prediction model for data access tier. Existing object-oriented metrics and complexity software metrics are used in the business tier prediction model. New sets of metrics have been proposed for the presentation logic tier and data access tier. These metrics are validated using data extracted from real world applications. The trained models can be used as tools to assist software mangers in making software release decisions.     

组合核相关向量机的软件可靠性预测模型

可靠性作为衡量软件质量的一种重要特性,对软件管理具有重要的意义。针对单一核函数的缺陷,提出一种组合核函数相关向量机的软件可靠性预测模型。首先对当前软件可靠性研究现状进行分析,然后采用组合核函数相关向量机对训练集进行学习和建模,最后通过具体实例对模型的预测性能进行分析。结果表明,本模型获得了理想的软件可靠性预测结果,且其预测性能要优于单一核函数模型,在软件可靠性预测中有重要的应用价值。     

基于拟似然估计方法的软件失效预测模型

软件缺陷预测是软件可靠性研究的一个重要方向。由于影响软件失效的因素有很多,相互之间关联关系复杂,在分析建模中常用联合分布函数来描述,而实际应用中难以确定,直接影响软件失效预测。基于拟似然估计提出一种软件失效预测方法,通过主成分分析筛选影响软件失效的主要影响因素,建立多因素软件失效预测模型,利用这些影响因素的数字特征(均值函数和方差函数)以及采用拟似然估计方法估计出模型参数,进而对软件失效进行预测分析。基于两个真实数据集Eclipse JDT和Eclipse PDE,与经典Logistic回归和Probit回归预测模型进行实验对比分析,结果表明采用拟似然估计对软件缺陷预测具有可行性,且预测精度均优于这两种经典回归预测模型。     

Evolutionary fuzzy SVR modeling of weld residual stress

Residual stresses are an integral part of the total stress acting on any component in service. It is important to determine and/or predict the magnitude, nature and direction of the residual stress to estimate the life of important engineering parts, particularly welded components. Researchers have developed many direct measuring techniques for welding residual stress. Intelligent techniques have been developed to predict residual stresses to meet the demands of advanced manufacturing planning. This research paper explores the development of Finite Element model and evolutionary fuzzy support vector regression model for the prediction of residual stress in welding. Residual stress model is developed using Finite Element Simulation. Results from Finite Element Method (FEM) model are used to train and test the developed Fuzzy Support Vector Regression model tuned with Genetic Algorithm (FSVRGA) using K-fold cross validation method. The performance of the developed model is compared with Support Vector Regression model and Fuzzy Support Vector Regression model. The proposed and developed model is superior in terms of computational speed and accuracy. Developed models are validated and reported. The developed model finds scope in setting the initial weld process parameters.     

Novel yield model for integrated circuits with clustered defects

As wafer sizes increase, the clustering phenomenon of defects increases. Clustered defects cause the conventional Poisson yield model underestimate actual wafer yield, as defects are no longer uniformly distributed over a wafer. Although some yield models, such as negative binomial or compound Poisson models, consider the effects of defect clustering on yield prediction, these models have some drawbacks. This study presents a novel yield model that employs General Regression Neural Network (GRNN) to predict wafer yield for integrated circuits (IC) with clustered defects. The proposed method utilizes five relevant variables as input for the GRNN yield model. A simulated case is applied to demonstrate the effectiveness of the proposed model.     

基于软件度量的Solidity智能合约缺陷预测方法

随着区块链技术的兴起,智能合约安全问题被越来越多的研究者和企业重视,目前已有一些针对智能合约缺陷检测技术的研究.软件缺陷预测技术是软件缺陷检测技术的有效补充,能够优化测试资源分配,提高软件测试效率.然而,目前还没有针对智能合约的软件缺陷预测研究.针对这一问题,提出了面向Solidity智能合约的缺陷预测方法.首先,设计了一组针对Solidity智能合约特有的变量、函数、结构和Solidity语言特性的度量元集(smart contract-Solidity, SC-Sol度量元集),并将其与重点考虑面向对象特征的度量元集(code complexity and features of object-oriented program, COOP度量元集)组合为COOP-SC-Sol度量元集.然后,从Solidity智能合约代码中提取相关度量元信息,并结合缺陷检测结果,构建Solidity智能合约缺陷数据集.在此基础上,应用了7种回归模型和6种分类模型进行Solidity智能合约的缺陷预测,以验证不同度量元集和不同模型在缺陷数量和倾向性预测上的性能差异.实验结果表明,相对于COOP度量元集...     

On the machining induced residual stresses in IN718 nickel-based alloy: Experiments and predictions with finite element simulation

Residual stresses after machining processes on nickel-based super alloys is of great interest to industry in controlling surface integrity of the manufactured critical structural components. Therefore, this work is concerned with machining induced residual stresses and predictions with 3-D Finite Element (FE) based simulations for nickel-based alloy IN718. The main methods of measuring residual stresses including diffraction techniques have been reviewed. The prediction of machining induced stresses using 3-D FE simulations and comparison of experimentally measured residual stresses for machining of IN718 have been investigated. The influence of material flow stress and friction parameters employed in FE simulations on the machining induced stress predictions have been also explored. The results indicate that the stress predictions have significant variations with respect to the FE simulation model and these variations can be captured and the resultant surface integrity can be better represented in an interval. Therefore, predicted residual stresses at each depth location are given in an interval with an average and standard deviation.     

基于CBR-RBR的工程争端诉讼结果预测

工程项目争端频发,双方最终有可能通过诉讼解决争端。但诉讼成本高昂,破坏双方友好合作关系并影响公司声誉,因此遭到很多学者的反对。为了避免争端双方盲目通过法律途径解决争端,试图对工程争端诉讼结果进行预测,希望争端双方在诉诸法律之前能够认识到自身行为的合理性。提出通过CBR-RBR方法对工程争端诉讼结果进行预测,并首次尝试对预测结果进行数量化处理,以提高CBR-RBR系统的实际应用价值。在此基础上,以“工程缺陷争端”这一争端类型进行实例研究,结果表明该方法对工程缺陷争端结果预测的准确率高达93.75%,而且最终数量结果的偏差也在预期范围之内,说明提出的CBR-RBR方法可以有效地应用于工程争端诉讼结果的预测,为双方解决争端提供科学依据。     

基于软件开发过程的软件缺陷管理研究

分析了软件缺陷管理的理论、方法及业务流程,指出了传统软件缺陷管理模式的不足。在此基础上提出了基于软件开发过程的软件缺陷管理模式,此模型在考虑测试者、开发者和评审者的基础上依据软件生命周期各阶段对缺陷进行了管理。最后对此管理模型的业务流程进行了详细的分析及应用,指出此管理模式很好的达到了尽早发现、预防与排除缺陷,改进软件过程的目的。     

基于软件信息库挖掘的软件缺陷预测方法

通过软件缺陷预测可以指导软件开发过程中资源的分配,提高软件质量和软件可靠性。为了更好地利用软件开发过程中产生的数据,指导软件的开发,在介绍了软件缺陷管理,数据挖掘,软件开发信息库知识的基础上,将数据挖掘的知识应用到软件开发信息库中,从版本信息库和缺陷跟踪系统中提取相关数据,经过预处理后这些数据就成数据挖掘技术的研究对象,通过选取合适的软件度量元,利用这些度量元建立新的软件缺陷预测模型并验证了该模型的有效性。     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

基于改进级联R-CNN的酒瓶瑕疵检测

为降低厂家因瓶装酒瑕疵带来的不必要损失,提出一种改进的Cascade R-CNN算法模型,对酒瓶瑕疵进行检测。采用基于聚类算法的Anchor生成策略,将多尺度预测的骨干网络用作特征提取,使用感兴趣对齐层取代原先的感兴趣池化层。将改进的模型与其它基于Faster R-CNN和Cascade R-CNN的酒瓶瑕疵检测模型做消融实验,实验结果表明,该模型能够更加准确识别和定位出多类酒瓶瑕疵情况。在检测速度方面虽然略逊于其它模型,但模型检测的准确度达到了79.6%,远高于其它模型。     

基于连续型深度置信神经网络的软件可靠性预测

为了提高软件可靠性智能预测的精度,采用连续型深度置信神经网络算法用于软件可靠性预测。首先提取影响软件可靠性的核心要素样本,并获取样本要素的关键特征;然后建立连续型深度置信神经网络(Deep Belief Network,DBN)的软件可靠性预测模型,输入待预测样本,通过多个受限波尔兹曼机(Restricted Boltzmann Machine,RBM)层的预处理训练,以及多次反向微调迭代获取DBN权重等参数,直到达到最大RBM层数和最大反向微调迭代次数;最后获得稳定的软件可靠性预测模型。实验结果证明,通过合理设置DBN隐藏层节点数和学习速率,可以获得良好的软件可靠性预测准确率和标准差。与常用的软件可靠性预测算法相比,所提算法的预测准确度高且标准差小,在软件可靠性预测方面的适用度较高。