Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Graph-Based Feature Learning for Cross-Project Software Defect Prediction

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects.The existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for CPDP.This paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source code.The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive models.The process involves graph construction,feature learning through graph embedding and LSTM,and defect prediction.Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So how to predict the defects quickly and accurately on the software change has become an important problem for software developers.Current defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal enough.Therefore,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect prediction.To evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and PostgreSQL.We compare the ITNB model with the transfer Naive Bayesian(TNB)model.The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

Unsupervised Domain Adaptation Based on Discriminative Subspace Learning for Cross-Project Defect Prediction

:Cross-project defect prediction(CPDP)aims to predict the defects on target project by using a prediction model built on source projects.The main problem in CPDP is the huge distribution gap between the source project and the target project,which prevents the prediction model from performing well.Most existing methods overlook the class discrimination of the learned features.Seeking an effective transferable model from the source project to the target project for CPDP is challenging.In this paper,we propose an unsupervised domain adaptation based on the discriminative subspace learning(DSL)approach for CPDP.DSL treats the data from two projects as being from two domains and maps the data into a common feature space.It employs crossdomain alignment with discriminative information from different projects to reduce the distribution difference of the data between different projects and incorporates the class discriminative information.Specifically,DSL first utilizes subspace learning based domain adaptation to reduce the distribution gap of data between different projects.Then,it makes full use of the class label information of the source project and transfers the discrimination ability of the source project to the target project in the common space.Comprehensive experiments on five projects verify that DSL can build an effective prediction model and improve the performance over the related competing methods by at least 7.10%and 11.08%in terms of G-measure and AUC.

基于知识图谱的跨项目安全缺陷报告预测方法

安全缺陷报告可以描述软件产品中的安全关键漏洞.为了消除软件产品的安全攻击风险,安全缺陷报告(security bug report,SBR)预测越来越受到研究人员的关注.但在实际软件开发场景中,需要进行软件安全漏洞预测的项目可能是来自新公司或属于新启动的项目,没有足够的已标记安全缺陷报告供在实践中构建此软件安全漏洞预测模型.一种简单的解决方案就是使用迁移模型,即利用其他项目已经标记过的数据来构建预测模型.受到该领域最近的两项研究工作的启发,以安全关键字过滤为思路提出一种融合知识图谱的跨项目安全缺陷报告预测方法KG-SBRP(knowledge graph of security bug report prediction).使用安全缺陷报告中的文本信息域结合CWE(common weakness enumeration)与CVE Details(common vulnerabilities and exposures)共同构建三元组规则实体,以三元组规则实体构建安全漏洞知识图谱,在图谱中结合实体及其关系识别安全缺陷报告.将数据分为训练集和测试集进行模型拟合和性能评估.所构建的模型在7个不同规模的安全缺陷报告数据集上展开实证研究,研究结果表明,所提方法与当前主流方法FARSEC和Keyword matrix相比,在跨项目安全缺陷报告预测场景下,性能指标F1-score值可以平均提高11%,除此之外,在项目内安全缺陷报告预测场景下,F1-score值同样可以平均提高30%.

“实验设计与数据处理”教学设计与实践

基于成果导向教育理念,依据“实验设计与数据处理”课程所支撑的毕业要求观测点,反向设计课程目标。围绕课程目标整合教学内容和教学资源、设计教学过程、组织教学活动、评价学习成效,并基于评价结果持续改进,形成“实施-评价-反馈-改进”闭环。教学过程引入“工艺优化驱动教学”的项目式教学法,与“专业综合实验”“科研实践”等实践环节形成跨课程教学联动,以实践环节中的实验方案设计和数据处理的实际需求推动“实验设计与数据处理”课程的理论教学和课下实践,将零散的知识体系化,碎片化的应用系统化,着力提升学生实验方案设计、实验结果统计分析和模拟预测以及方案优化能力。

悖论式领导对建设项目员工韧性的跨层次影响

在建设项目复杂多变的环境中,员工须具备高度韧性才能实现工作适应与个人发展。基于悖论理论与社会认同理论,以组织认同和悖论心态为中介变量,领导愿景为调节变量,构建了悖论式领导影响员工韧性的跨层次结构模型。运用多层线性模型方法对来自52个建设项目团队的问卷数据进行分析。结果表明:悖论式领导、组织认同、悖论心态对员工韧性均具有显著正向影响;悖论式领导对组织认同与悖论心态具有显著正向影响;悖论心态在悖论式领导与员工韧性的关系中起部分中介作用,而组织认同的中介作用不显著;领导愿景对悖论式领导与悖论心态的关系具有正向调节作用,对悖论式领导与组织认同的关系不具备显著调节作用。研究结果为增强建设项目员工韧性提供了管理启示。

A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction

从外部来源软件的标记的数据投射在目标补偿有用数据的缺乏的跨工程的缺点预言(CPDP ) 使用投射，以便造一个有意义的分类模型。然而，在从来源和目标工程提取的软件特征之间的分发差距可能太大，不能为训练使混合数据有用。在这份报纸，我们建议基于簇的新奇方法 FeSCH (用簇的特征选择混合数据) 由特征选择减轻分发差别。FeSCH 包括二个阶段。用一个基于密度的聚类方法聚类阶段簇特征的特征，和特征选择分阶段执行用评价策略从每簇选择特征。为 CPDP，我们在第二个阶段设计三不同启发式的评价策略。调查 FeSCH 的预言性能，我们基于真实世界的软件工程设计实验，并且学习在 FeSCH 的设计选择的效果(例如评价策略，特征选择比率，和分类器) 。试验性的结果证明 FeSCH 的有效性。与最先进的基线方法相比，第一， FeSCH 完成更好的性能，它的表演是使用的分类器影响的更少。第二， FeSCH 由有效地越过特征范畴选择特征提高表演，并且为为缺点预言选择有用特征提供指南。

山东裕龙岛跨河道工程对沉积动力环境影响研究

根据裕龙岛附近海域水文和地形等实测资料,采用数值模拟的方法,模拟了研究区跨河道工程建设前后的水动力、冲淤和水交换等沉积动力特征,探讨了跨河道工程建设对裕龙岛内部沉积动力环境的影响。结果表明,跨河道工程建设使裕龙岛内部水道潮流流速整体减小,最大减小量超过20 cm/s,减弱了水道内水动力环境;工程建设对水道内冲淤环境产生一定影响,改变了水道的微侵蚀环境,部分地区发生淤积,最大年淤积量超过2 cm;工程建设明显影响水道内水交换,造成水交换率下降,部分区域水交换率低于60%。整体而言,经一河、经二河建设桥梁,纬一河建设管涵对水道内沉积动力环境影响相对较小,符合工程建设的实际要求。海岸工程建设应优先考虑海洋环境保护,实现工程和环境的协调发展。

开源软件缺陷的跨项目相关问题推荐方法

GitHub是著名的开源软件开发社区,支持开发人员在开源项目中使用问题追踪系统来处理问题.在软件缺陷问题的讨论过程中,开发人员可能指出与该缺陷问题相关的其他项目问题(称为跨项目相关问题),为缺陷问题的修复提供参考信息.然而,GitHub平台中托管了超过2亿的开源项目和12亿个问题,导致人工识别和获取跨项目相关问题的工作极其耗时.提出为缺陷问题自动化推荐跨项目相关问题的方法CPIRecom.为了构建预选集,采用项目之间历史相关问题对的数量和问题发布时间间隔筛选问题.其次,为了精准推荐,采用BERT预训练模型提取文本特征,分析项目特征.然后使用随机森林算法计算预选问题与缺陷问题的相关概率,最终根据相关概率排名得到推荐列表.模拟CPIRecom方法在GitHub平台的使用情况.CPIRecom方法的平均倒数排名达到0.603,前5项查全率达到0.715.

基于改进DBN的大断面矩形顶管穿越铁路风险评估

为了评估大断面矩形顶管隧道下穿铁路风险,采用解释结构模型法建立大断面矩形顶管下穿铁路风险评估体系,构建改进动态贝叶斯网络(DBN)风险预测模型,采用模糊集理论、专家评估法等得到大断面矩形顶管穿越铁路风险值。研究结果表明:改进的DBN模型可实现大断面矩形顶管穿越铁路工程的工前风险评估、风险控制预案提前布置、现场施工风险动态评估和反馈;改进DBN模型的风险预测与专家预测结果一致;有限元-动态贝叶斯网络(FEM-DBN)模型的预测风险值较现场监测-动态贝叶斯网络(OSM-DBN)模型稍大,可为穿越工程提供更多安全储备。研究结果可为大断面顶管穿越铁路施工风险控制提供参考。

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。

A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning

Software Defect Prediction(SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years.However,the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets.Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction(CPDP).In this work,we propose a transfer-leaning algorithm(TSboostDF) that considers both knowledge transfer and class imbalance for CPDP.The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.

新建道路与既有高速互通方案研究

以武汉临空大道与机场二高速为例,研究了新建公路与既有高速公路的互通方案,重点研究了互通工程中匝道线路方案比选、路面横断面布置、路基填方工程、路基防护、不良地质地段及特殊路基设计、新旧路面结构拼接设计、桥梁方案设计和桥面铺装工程等,对同类互通工程建设方案比选具有参考意义。

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

As the boom of mobile devices,Android mobile apps play an irreplaceable roles in people’s daily life,which have the characteristics of frequent updates involving in many code commits to meet new requirements.Just-in-Time(JIT)defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers,which is more suitable to mobile apps.As the within-app defect prediction needs sufficient historical data to label the commit instances,which is inadequate in practice,one alternative method is to use the cross-project model.In this work,we propose a novel method,called KAL,for cross-project JIT defect prediction task in the context of Android mobile apps.More specifically,KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features.Then,the adversarial learning technique is used to extract the common feature embedding for the model building.We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation.The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

Cross-project software defect prediction based on multi-source data sets

Cross-project defect prediction(CPDP) uses one or more source projects to build a defect prediction model and applies the model to the target project. There is usually a big difference between the data distribution of the source project and the target project, which makes it difficult to construct an effective defect prediction model. In order to alleviate the problem of negative migration between the source project and the target project in CPDP, this paper proposes an integrated transfer adaptive boosting(TrAdaBoost) algorithm based on multi-source data sets(MSITrA). The algorithm uses an existing two-stage data filtering algorithm to obtain source project data related to the target project from multiple source items, and then uses the integrated TrAdaBoost algorithm proposed in the paper to build a CPDP model. The experimental results of Promise’s 15 public data sets show that: 1) The cross-project software defect prediction model proposed in this paper has better performance in all tested CPDP methods;2) In the within-project software defect prediction(WPDP) experiment, the proposed CPDP method has achieved the better experimental results than the tested WPDP method.

基于DEMATEL方法的科学基金项目绩效关键影响因素识别及实证研究——以促进海峡两岸科技合作联合基金项目为例

基础研究是应用研究和重大创新之源,联合基金作为国家基金委联合地方政府支持基础研究的主渠道之一,对提升地区原始创新能力、促进人才队伍建设发挥重要作用。为了真实反映促进海峡两岸科技合作联合基金资助项目的学术水平,从创新成果、人才培养、合作交流、项目延伸和间接影响等5个维度构建结题项目绩效评价指标体系,运用DEMATEL法分析15个二级指标之间的关联影响,结合多层次模糊综合评价法构建模型,并以某大学承担的重点支持项目为案例对影响项目绩效的关键因素进行实证分析,确定这个项目绩效评价等级。实证结果表明:DEMATEL法可以识别影响项目绩效的关键指标;模糊综合评价模型既考虑了评价对象的模糊性,也兼顾了指标体系的层次性,综合评价结果更具科学性。

基于新交叉熵的灰关联双向投影多属性决策方法

文章针对属性权重完全未知的犹豫模糊多属性决策问题,提出一种基于新交叉熵的灰关联双向投影多属性决策方法。首先,结合基本不等式概念提出犹豫模糊数中新的交叉熵公式,并证明其满足熵的基本性质。其次,以新的交叉熵公式为距离公式,应用离差最大化方法求得属性权重。再次,将灰关联双向投影法拓展到一般犹豫模糊环境下,提出了基于新交叉熵的灰关联双向投影多属性决策方法。最后,通过一个算例验证了所提方法的可行性和有效性。该方法为解决犹豫模糊信息中属性权重完全未知的决策问题提供了新的方法和思路,且具有较高的区分度和灵敏度,提高了决策可信度。

基于TOE理论的数实经济深度融合影响因素模型构建与分析

文章运用TOE理论框架建立数实经济深度融合影响因素指标体系,采用ISM-ANP-MICMAC模型对数实经济融合影响因素的网络层级、优先级、驱动力及依赖性进行剖析,结果表明:数实经济深度融合影响因素之间存在着紧密且复杂的关联关系,影响因素网络层级结构中包含表层直接、中层过渡、深层关键及根层决定四个层次,优先级排序中数字技术创新、人才资源、基础设施、政府行为属于核心关键因素,数据要素、网络安全、国际合作、营商环境、数字化产业、数字经济治理属于次要因素,数字社会、数字政府、产业数字化因素驱动力较弱,但依赖性极强。基于此对数实经济融合提出短中长期发展对策,及时疏通融合路径中的急、难、重点,为推动数实经济深度融合高质量发展提供理论指导。

基于PDCA循环理论项目式实践教学方法研究

在项目式教学中引入PDCA循环理论,可以不断提升实践教学的质量。形成了项目计划阶段、项目执行阶段和项目改进及检查阶段共三个阶段,不断优化调整项目方案中的各个任务质量。以跨境电子商务课程为例,教学模式提供了具体设计与实施方法:进行线上线下实践,基于工作过程设计项目方案,小组合作完成项目方案,采用多元检查与评价,最后进行反思与总结。