Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

Software Defect Prediction Based Ensemble Approach

Software systems have grown significantly and in complexity.As a result of these qualities,preventing software faults is extremely difficult.Software defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance costs.When it comes to lowering software costs and assuring software quality,SDP plays a critical role in software development.As a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more efficiently.Several methods for detecting and addressing such flaws at a low cost have been offered.These approaches,on the other hand,need to be significantly improved in terms of performance.Therefore in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are proposed.The proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative features.To find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository datasets.When compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

Software Residual defects Prediction Model based on BBNs

The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.

A Framework for Software Defect Prediction Using Neural Networks

Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering.However,due to the limitations of current machine learning algorithms,we can’t achieve good effect for defect prediction by only using machine learning algorithms.In previous studies,some researchers used extreme learning machine(ELM)to conduct defect prediction.However,the initial weights and biases of the ELM are determined randomly,which reduces the prediction performance of ELM.Motivated by the idea of search based software engineering,we propose a novel software defect prediction model named KAEA based on kernel principal component analysis(KPCA),adaptive genetic algorithm,extreme learning machine and Adaboost algorithm,which has three main advantages:(1)KPCA can extract optimal representative features by leveraging a nonlinear mapping function;(2)We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM,so as to improve the generalization ability and prediction capacity of ELM;(3)We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor,which can further improve the effect of defect prediction.To effectively evaluate the performance of KAEA,we use eleven datasets from large open source projects,and compare the KAEA with four machine learning basic classifiers,ELM and its three variants.The experimental results show that KAEA is superior to these baseline models in most cases.

Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.

RFC:a feature selection algorithm for software defect prediction

Software defect prediction(SDP)is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects,so as to effectively predict defects in the new software.However,there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors.In order to identify and remove the redundant and irrelevant features in software defect datasets,we propose ReliefF-based clustering(RFC),a clusterbased feature selection algorithm.Then,the correlation between features is calculated based on the symmetric uncertainty.According to the correlation degree,RFC partitions features into k clusters based on the k-medoids algorithm,and finally selects the representative features from each cluster to form the final feature subset.In the experiments,we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration(NASA)software defect prediction datasets in terms of area under curve(AUC)and Fvalue.The experimental results show that RFC can effectively improve the performance of SDP.

Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study

An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.

Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

Developing successful software with no defects is one of the main goals of software projects.In order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital role.Machine learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated structure.In this paper,we aim to address such issues in predicting software defects.We propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects prone.We design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets.The experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software defects.According to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering.This,in turn,could lead to saving software development resources and producing more reliable software.

Software Defect Prediction Method Based on Rule Knowledge Extraction Model

The software defects are managed through the knowledge base,and defect management is upgraded from the data level to the knowledge level. The rule knowledge is mined from bug data based on a rule-based knowledge extraction model,and the appropriate strategy is configured in the strategy layer to predict software defects. The model is extracted by direct association rules and extended association rules,which improve the prediction rate of related defects and the efficiency of software testing.

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

工作量感知软件缺陷预测中偏斜分布的影响及测试评估方法

针对工作量感知软件缺陷预测中传统模型测试评估方法存在偏差这一问题,采用偏斜分布的偏度作为数值特征,研究了3种主要测试评估方法的测试集在工作量偏度的偏差和与其对应的估计误差,并基于偏度偏差较小的采样余量方法,提出一种改进方法——后采样方法,所提后采样方法能够保持测试集的类标签比例以避免生成无效测试集。研究结果表明:最常用的十折交叉验证方法偏度偏差最大,其估计误差也最大;与十折交叉验证相比,改进方法性能估计误差减少约4.9%~26.9%;与采样余量方法相比,改进方法不会产生无效测试集,并证明了减小测试集偏度偏差以减少估计误差的有效性。所提后采样方法为工作量感知软件缺陷预测提供了一种更可靠的测试评估方法,能够更准确地评估模型性能。

基于联合特征分布匹配的跨项目缺陷预测

为解决跨项目软件缺陷预测研究中存在的特征不完备和分类边界模糊问题,提出一种基于联合特征的双编码器分布匹配方法(DeDM-JF)。利用卷积神经网络提取代码中与缺陷有关的结构语义特征,将其与人为选取的Handcrafted特征结合,形成联合特征;在此基础上,构建包含分布差异匹配层的双自编码器,学习跨项目全局和局部可迁移特征用于训练缺陷预测模型。面向软件缺陷数据仓库中的798对跨项目缺陷预测任务开展实验,与相关的跨项目缺陷预测方法比较,DeDM-JF方法预测的F-measure和MCC指标有明显提升。

基于加权复杂度的SMOTE算法及其在软件缺陷预测中的应用

近年来,SMOTE被广泛应用于软件缺陷预测中不平衡数据的处理。然而,现有的SMOTE算法普遍忽视了不同样本的复杂度存在很大差异这一问题。事实上,在缺陷预测时样本的复杂度与其是否具有缺陷之间存在着密切的联系,因此,在进行过采样时,有必要利用样本的复杂度来辅助新样本的合成,从而提高缺陷预测的性能。如何度量样本的复杂度非常重要,论文在计算样本复杂度时充分考虑到每一个条件属性的权重,从而得到一种加权复杂度的概念。基于加权复杂度,提出一种新的SMOTE算法——WCP-SMOTE,并将其应用于软件缺陷预测。WCP-SMOTE算法首先利用粗糙集中的粒度决策熵来计算决策表中每个条件属性的重要性和权重;其次,通过对样本在所有属性上的取值进行加权求和,从而得到该样本的加权复杂度;第三,根据加权复杂度对少数类样本进行升序排序,并从头到尾对相邻的两个少数类样本求平均来不断地合成新的样本,直到获得一个平衡的数据集。在多个缺陷预测数据集上的实验表明,利用WCP-SMOTE算法来处理不平衡数据能够获得更好的软件缺陷预测性能。

基于多源特征门控融合的软件缺陷预测

随着当前软件开发规模的增大和复杂度的不断提高,如何在保证效率的同时提高软件质量成为软件工程领域研究的重点和难点.软件缺陷预测是软件质量保障的重要研究方向,旨在帮助软件从业人员预测软件产品中潜在的缺陷模块,从而更有效地分配测试资源.已有研究主要提取软件特征来建立缺陷预测模型,但通常仅使用单一类型特征作为模型输入,并且缺乏特征的有效融合,导致缺陷预测的性能有待提高.提出了一种基于多源特征门控融合的软件缺陷预测方法(DP⁃GM),首先利用抽象语法树和词嵌入模型得到代码语义表示;然后,采用门控循环单元(GRU)对语义特征向量和传统特征向量进行特征提取;最后,利用门控机制融合多源特征来训练模型并进行软件缺陷预测.实验结果表明,与当前具有代表性的三个基线方法相比较,提出的方法在召回率和F1值分别高出最优基线方法35.3%和10.5%.因此,提出的方法可提升软件缺陷预测的准确性,帮助软件从业者提高开发效益.

膜内麻雀优化ELM的软件缺陷预测算法

原始麻雀搜索算法存在寻优精度低、迭代后期容易陷入局部极值的问题,结合高效寻优性能的改进麻雀搜索算法和具有并行计算能力的膜计算,提出一种膜内麻雀优化算法(IMSSA)。在10个CEC2017测试函数上的实验结果表明,IMSSA具有更高的寻优精度。为进一步验证IMSSA的性能,使用IMSSA优化极限学习机(ELM)参数,提出一种膜内麻雀优化ELM(IMSSA-ELM)算法,并将其应用于软件缺陷预测领域。实验结果表明:在15个公开的软件缺陷数据集中,IMSSA-ELM算法预测性能在G-mean、MCC这2个评价指标下明显优于其他4种先进的对比算法,表明IMSSA-ELM算法具有更好的预测精度和稳定性,其实验结果在Friedman ranking和Holm’s post-hoc test非参数检验中具有明显的统计显著性。

基于TMFG生成拓扑图的软件缺陷预测图特征选择方法

软件缺陷预测是降低软件测试成本的重要手段,而特征选择则是其中关键的一环.然而,传统的特征选择算法局限于考虑特征之间的双边关系和两两特征的关联,而无法有效处理更为复杂的多边关系和多向交互等问题.为此,提出了一种基于TMFG的软件缺陷预测图特征选择方法.该方法首先将拓扑图引入特征选择算法中,利用对称不确定性作为特征关联度,将特征表示为拓扑图的节点,构建特征全连接图.然后,通过TMFG去连边算法去除全连接图中的部分连边,并进行图聚类操作.接着,对每个聚类中的特征进行排序,并从每个类中选取特定数目的特征进行综合,得到最终的特征子集.最后,通过在Promise数据仓库中的数据集上进行对比实验,结果表明,所提出的方法在进一步优化特征选择选出的特征子集的质量方面取得了良好的效果,尤其在数据量较大的数据集中表现出更大的优势.

基于贝叶斯网络集成的软件缺陷预测

针对常用的软件缺陷预测模型缺乏可解释性及鲁棒性的问题,为了推断和理解软件缺陷预测中变量间的相关关系,研究了贝叶斯网络在软件缺陷预测中的应用方法,建立了贝叶斯网络软件缺陷预测模型及集成软件缺陷预测模型。使用数据离散化方法处理数据,采用贝叶斯网络结构学习算法确定网络结构及参数,并利用贝叶斯网络推断软件缺陷的概率分布;将贝叶斯网络与K近邻、决策树、逻辑回归等软件缺陷预测器以软投票的方式集成,建立集成软件缺陷预测模型;在6个公开的软件缺陷数据集上进行实验仿真。实验结果表明,与常用的集成软件缺陷预测模型相比所建立的基于贝叶斯网络的集成软件缺陷预测模型在F1、Recall、G-Mean评价指标上表现出了更好的预测性能。从因果分析的角度,为软件缺陷预测探索一条新的研究思路。