基于复杂性的软件缺陷预测

为解决软件缺陷预测问题引入了最小二乘支持向量机算法(LS-SVM),加速了超参数的选择过程,给出了逐个加入新的样本用以模型校正的快捷方法,以软件复杂性度量为线索,建立了基于FLS-SVM的软件缺陷预测模型。通过具体实例阐明了模型的执行过程及小样本情况下比神经网络更为出色的预测能力,并根据回归方程指出了对软件缺陷影响显著的复杂性度量。     

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.

基于生命周期的软件缺陷预测技术

为保证软件可靠性和软件质量,在基于软件开发周期的基础上,提出了一种利用PCA-BP模糊神经网络的软件缺陷预计方法.针对影响软件可靠性的各种因素,依据相关的标准,结合工程实践,选取了影响软件可靠性的度量元.收集了实际工程中的一类飞行控制软件的度量数据,利用提出的模型进行缺陷预测,并将预测结果与传统的BP神经网络模型计算的结果进行了对比.对比结果表明,与基于BP神经网络的预测方法相比较,结合了主成分分析方法的PCA-BP神经网络预测方法具有更快的收敛速度和更高的预测准确度.     

基于代理辅助多目标萤火虫算法的软件缺陷预测方法研究

针对软件缺陷预测中数据维度的复杂化和类不平衡问题,提出一种基于代理辅助模型的多目标萤火虫算法(SMO-MSFFA)的软件缺陷预测方法。该方法采用了多组策略萤火虫算法(MSFFA),以最小化数据的特征选择比率和最大化模型评测AUC值为多目标目标函数,分别以随机森林(RF)、支持向量机(SVM)和K近邻分类算法(KNN)为分类器构建软件缺陷预测模型。考虑到进化算法自身的迭代特点,嵌入代理模型离线完成部分个体评价函数的计算,以缩短计算耗时。在公开数据集NASA中的PC1、KC1和MC1项目上进行实验验证,与NSGA-II方法相比,在项目PC1、KC1和MC1上模型AUC均值分别提升0.17、降低0.01和提升0.09,平均特征选择比率分别降低0.08,0.17和0.05,平均耗时分别增加131 s,降低了199 s和降低了431 s。实验结果表明,提出的方法在提高模型性能、降低特征选择比率和缩短计算耗时方面具有明显的优势。     

一种基于主题模型的软件缺陷预测技术研究

软件缺陷预测通常针对代码表面特征训练预测模型并对新样本进行预测,忽视了代码背后隐藏的不同技术方面和主题,从而导致预测不准确。针对这种问题,提出了一种基于主题模型的软件缺陷预测方法。将软件代码库视为不同技术方面和主题的集合,不同的主题或技术方面有不同的缺陷倾向。采用LDA主题模型对不同主题及其缺陷倾向进行建模,根据建模结果计算主题度量,并将传统度量方式和主题度量结合进行模型训练和预测。实验结果显示,该方法相对传统的软件缺陷预测技术有高的准确性,并且可以在软件演化中保证模型相对稳定,可以适用于各种缺陷预测任务。     

Just-in-time defect prediction for software hunks

Just-in-time defect prediction can remind software developers and managers to verify and fix bugs at the moment they appeared, thus improving the effectiveness and validity of bug fixing. Existing studies mainly focus on just-in-time prediction for software files (JIT-F). JIT-F is a binary classification problem, which classifies (hence predicts) a file change as buggy or clean. This article provides a detailed analysis of just-in-time defect prediction for software hunks (JIT-H), which predicts bugs at a finer level of granularity, and hence further improves the efficiency of bug fixing. Classification is performed using the ensemble technique of bagging—aggregated combinations of random under sampling plus multiple classifiers (J48 and Random Forest). An empirical study with 10 open source projects was conducted to validate the effectiveness of JIT-H. Experimental results show that JIT-H is effective at predicting defects in software hunk changes. Compared with JIT-F, JIT-H is more cost effective. Additionally, analysis on the change features indicates that Text Vector features and hunk change level features are of more importance than features in other groups and levels.     

A dynamic capture–recapture model for software defect prediction

Several models have been developed that attempt to predict the total number of defects in a software product. One such approach uses the capture–recapture model, a technique employed by biologists for predicting wildlife populations. In this method once the software is built and defects begin to be identified a prediction can be made for the total number of software defects present. But capture–recapture models rely on expert inspectors and the technique cannot be employed once the software has been released. The work reported here extends the capture–recapture technique to the post-inspection phase and to where inspection data is unavailable, by using user defect reports. The proposed technique does not rely on expert inspectors and is particularly suitable for open source software.     

A comprehensive evaluation of capture-recapture models forestimating software defect content

An important requirement to control the inspection of software artifacts is to be able to decide, based on more objective information, whether the inspection can stop or whether it should continue to achieve a suitable level of artifact quality. A prediction of the number of remaining defects in an inspected artifact can be used for decision making. Several studies in software engineering have considered capture-recapture models to make a prediction. However, few studies compare the actual number of remaining defects to the one predicted by a capture-recapture model on real software engineering artifacts. The authors focus on traditional inspections and estimate, based on actual inspections data, the degree of accuracy of relevant state-of-the-art capture-recapture models for which statistical estimators exist. In order to assess their robustness, we look at the impact of the number of inspectors and the number of actual defects on the estimators' accuracy based on actual inspection data. Our results show that models are strongly affected by the number of inspectors, and therefore one must consider this factor before using capture-recapture models. When the number of inspectors is too small, no model is sufficiently accurate and underestimation may be substantial. In addition, some models perform better than others in a large number of conditions and plausible reasons are discussed. Based on our analyses, we recommend using a model taking into account that defects have different probabilities of being detected and the corresponding Jackknife Estimator. Furthermore, we calibrate the prediction models based on their relative error, as previously computed on other inspections. We identified theoretical limitations to this approach which were then confirmed by the data     

Transfer learning for cross-company software defect prediction

Context

Software defect prediction studies usually built models using within-company data, but very few focused on the prediction models trained with cross-company data. It is difficult to employ these models which are built on the within-company data in practice, because of the lack of these local data repositories. Recently, transfer learning has attracted more and more attention for building classifier in target domain using the data from related source domain. It is very useful in cases when distributions of training and test instances differ, but is it appropriate for cross-company software defect prediction?

Objective

In this paper, we consider the cross-company defect prediction scenario where source and target data are drawn from different companies. In order to harness cross company data, we try to exploit the transfer learning method to build faster and highly effective prediction model.

Method

Unlike the prior works selecting training data which are similar from the test data, we proposed a novel algorithm called Transfer Naive Bayes (TNB), by using the information of all the proper features in training data. Our solution estimates the distribution of the test data, and transfers cross-company data information into the weights of the training data. On these weighted data, the defect prediction model is built.

Results

This article presents a theoretical analysis for the comparative methods, and shows the experiment results on the data sets from different organizations. It indicates that TNB is more accurate in terms of AUC (The area under the receiver operating characteristic curve), within less runtime than the state of the art methods.

Conclusion

It is concluded that when there are too few local training data to train good classifiers, the useful knowledge from different-distribution training data on feature level may help. We are optimistic that our transfer learning method can guide optimal resource allocation strategies, which may reduce software testing cost and increase effectiveness of software testing process.     

偏相关方法在软件缺陷预测中的应用*

为了提高预测模型的性能,解决不同属性子集带来的分歧,提出了基本偏相关方法的预测模型。首先,该方法在公开数据集上分析出代码静态属性与缺陷数之间存在偏相关关系;然后基于偏相关系数值,计算出代码复杂性度密度属性值;最后基于该属性值建立新的缺陷预测模型。实验表明,该模型具有较高的召回率和很好的F-measure性能,从而进一步证实了代码属性与模块缺陷之间的偏相关性是影响软件质量预测性能的重要因素的结论。该结论有助于建立更加稳定可靠的软件缺陷预测模型。     

软件缺陷预测中的数据预处理方法

软件缺陷预测是软件质量保障领域的热点研究课题，缺陷预测模型的质量与训练数据有密切关系。用于缺陷预测的数据集主要存在数据特征的选择和数据类不平衡问题。针对数据特征选择问题，采用软件开发常用的过程特征和新提出的扩展过程特征，然后采用基于聚类分析的特征选择算法进行特征选择；针对数据类不平衡问题，提出改进的Borderline-SMOTE过采样方法，使得训练数据集的正负样本数量相对平衡且合成样本的特征更符合实际样本特征。采用bugzilla、jUnit等项目的开源数据集进行实验，结果表明：所采用的特征选择算法在保证模型F-measure值的同时，可以降低57.94%的模型训练时间；使用改进的Borderline-SMOTE方法处理样本得到的缺陷预测模型在Precision、Recall、F-measure、AUC指标上比原始方法得到的模型平均分别提高了2.36个百分点、1.8个百分点、2.13个百分点、2.36个百分点；引入了扩展过程特征得到的缺陷预测模型比未引入扩展过程特征得到的模型在F-measure值上平均提高了3.79%；与文献中的方法得到的模型相比，所提方法得到的模型在F-measure值上平均提高了15.79%。实验结果证明所提方法能有效提升缺陷预测模型的质量。     

软件缺陷预测中的数据预处理方法

软件缺陷预测是软件质量保障领域的热点研究课题,缺陷预测模型的质量与训练数据有密切关系。用于缺陷预测的数据集主要存在数据特征的选择和数据类不平衡问题。针对数据特征选择问题,采用软件开发常用的过程特征和新提出的扩展过程特征,然后采用基于聚类分析的特征选择算法进行特征选择;针对数据类不平衡问题,提出改进的Borderline-SMOTE过采样方法,使得训练数据集的正负样本数量相对平衡且合成样本的特征更符合实际样本特征。采用bugzilla、jUnit等项目的开源数据集进行实验,结果表明：所采用的特征选择算法在保证模型F-measure值的同时,可以降低57.94%的模型训练时间;使用改进的Borderline-SMOTE方法处理样本得到的缺陷预测模型在Precision、Recall、F-measure、AUC指标上比原始方法得到的模型平均分别提高了2.36个百分点、1.8个百分点、2.13个百分点、2.36个百分点;引入了扩展过程特征得到的缺陷预测模型比未引入扩展过程特征得到的模型在F-measure值上平均提高了3.79%;与文献中的方法得到的模型相比,所提方法得到的模型在F-measure值上平均提高了15.79%。实验结果证明所提方法能有效提升缺陷预测模型的质量。     

An analysis of several software defect models

Results are presented of an analysis of several defect models using data collected from two large commercial projects. Traditional models typically use either program matrices (i.e. measurements from software products) or testing time or combinations of these as independent variables. The limitations of such models have been well-documented. The models considered use the number of defects detected in the earlier phases of the development process as the independent variable. This number can be used to predict the number of defects to be detected later, even in modified software products. A strong correlation between the number of earlier defects and that of later ones was found. Using this relationship, a mathematical model was derived which may be used to estimate the number of defects remaining in software. This defect model may also be used to guide software developers in evaluating the effectiveness of the software development and testing processes     

Mining software repositories for comprehensible software fault prediction models

Software managers are routinely confronted with software projects that contain errors or inconsistencies and exceed budget and time limits. By mining software repositories with comprehensible data mining techniques, predictive models can be induced that offer software managers the insights they need to tackle these quality and budgeting problems in an efficient way. This paper deals with the role that the Ant Colony Optimization (ACO)-based classification technique AntMiner+ can play as a comprehensible data mining technique to predict erroneous software modules. In an empirical comparison on three real-world public datasets, the rule-based models produced by AntMiner+ are shown to achieve a predictive accuracy that is competitive to that of the models induced by several other included classification techniques, such as C4.5, logistic regression and support vector machines. In addition, we will argue that the intuitiveness and comprehensibility of the AntMiner+ models can be considered superior to the latter models.     

An effort prediction framework for software defect correction

This article tackles the problem of predicting effort (in person–hours) required to fix a software defect posted on an Issue Tracking System. The proposed method is inspired by the Nearest Neighbour Approach presented by the pioneering work of Weiss et al. (2007) [1]. We propose four enhancements to Weiss et al. (2007) [1]: Data Enrichment, Majority Voting, Adaptive Threshold and Binary Clustering. Data Enrichment infuses additional issue information into the similarity-scoring procedure, aiming to increase the accuracy of similarity scores. Majority Voting exploits the fact that many of the similar historical issues have repeating effort values, which are close to the actual. Adaptive Threshold automatically adjusts the similarity threshold to ensure that we obtain only the most similar matches. We use Binary Clustering if the similarity scores are very low, which might result in misleading predictions. This uses common properties of issues to form clusters (independent of the similarity scores) which are then used to produce the predictions. Numerical results are presented showing a noticeable improvement over the method proposed in Weiss et al. (2007) [1].     

Multiple kernel ensemble learning for software defect prediction

Software defect prediction aims to predict the defect proneness of new software modules with the historical defect data so as to improve the quality of a software system. Software historical defect data has a complicated structure and a marked characteristic of class-imbalance; how to fully analyze and utilize the existing historical defect data and build more precise and effective classifiers has attracted considerable researchers’ interest from both academia and industry. Multiple kernel learning and ensemble learning are effective techniques in the field of machine learning. Multiple kernel learning can map the historical defect data to a higher-dimensional feature space and make them express better, and ensemble learning can use a series of weak classifiers to reduce the bias generated by the majority class and obtain better predictive performance. In this paper, we propose to use the multiple kernel learning to predict software defect. By using the characteristics of the metrics mined from the open source software, we get a multiple kernel classifier through ensemble learning method, which has the advantages of both multiple kernel learning and ensemble learning. We thus propose a multiple kernel ensemble learning (MKEL) approach for software defect classification and prediction. Considering the cost of risk in software defect prediction, we design a new sample weight vector updating strategy to reduce the cost of risk caused by misclassifying defective modules as non-defective ones. We employ the widely used NASA MDP datasets as test data to evaluate the performance of all compared methods; experimental results show that MKEL outperforms several representative state-of-the-art defect prediction methods.     

Time variance and defect prediction in software projects

It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project’s goals. In this paper we first verify the existence of variability in a bug prediction model’s accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a high variability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models’ accuracy in advance.     

Classification with reject option for software defect prediction

ContextSoftware defect prediction (SDP) is an important task in software engineering. Along with estimating the number of defects remaining in software systems and discovering defect associations, classifying the defect-proneness of software modules plays an important role in software defect prediction. Several machine-learning methods have been applied to handle the defect-proneness of software modules as a classification problem. This type of “yes” or “no” decision is an important drawback in the decision-making process and if not precise may lead to misclassifications. To the best of our knowledge, existing approaches rely on fully automated module classification and do not provide a way to incorporate extra knowledge during the classification process. This knowledge can be helpful in avoiding misclassifications in cases where system modules cannot be classified in a reliable way.ObjectiveWe seek to develop a SDP method that (i) incorporates a reject option in the classifier to improve the reliability in the decision-making process; and (ii) makes it possible postpone the final decision related to rejected modules for an expert analysis or even for another classifier using extra domain knowledge.MethodWe develop a SDP method called rejoELM and its variant, IrejoELM. Both methods were built upon the weighted extreme learning machine (ELM) with reject option that makes it possible postpone the final decision of non-classified modules, the rejected ones, to another moment. While rejoELM aims to maximize the accuracy for a rejection rate, IrejoELM maximizes the F-measure. Hence, IrejoELM becomes an alternative for classification with reject option for imbalanced datasets.ResultsrejoEM and IrejoELM are tested on five datasets of source code metrics extracted from real world open-source software projects. Results indicate that rejoELM has an accuracy for several rejection rates that is comparable to some state-of-the-art classifiers with reject option. Although IrejoELM shows lower accuracies for several rejection rates, it clearly outperforms all other methods when the F-measure is used as a performance metric.ConclusionIt is concluded that rejoELM is a valid alternative for classification with reject option problems when classes are nearly equally represented. On the other hand, IrejoELM is shown to be the best alternative for classification with reject option on imbalanced datasets. Since SDP problems are usually characterized as imbalanced learning problems, the use of IrejoELM is recommended.     

基于CS-ANN的软件缺陷预测模型研究

为了提高软件缺陷预测的准确率,利用布谷鸟搜索算法(Cuckoo Search,CS)的寻优能力和人工神经网络算法（Artificial Neural Network,ANN）的非线性计算能力,提出了基于CS-ANN的软件缺陷预测方法。此方法首先使用基于关联规则的特征选择算法降低数据的维度,去除了噪声属性;利用布谷鸟搜索算法寻找神经网络算法的权值,然后使用权值和神经网络算法构建出预测模型;最后使用此模型完成缺陷预测。使用公开的NASA数据集进行仿真实验,结果表明该模型降低了误报率并提高了预测的准确率,综合评价指标AUC（area under the ROC curve）、F1值和G-mean都优于现有模型。     

基于LASSO-SVM的软件缺陷预测模型研究

针对当前大多数软件缺陷预测模型预测准确率较差的问题, 提出了结合最小绝对值压缩和选择方法与支持向量机算法的软件缺陷预测模型。首先利用最小绝对值压缩与选择方法的特征选择能力降低了原始数据集的维度, 去除了与软件缺陷预测不相关的数据集; 然后利用交叉验证算法的参数寻优能力找到支持向量机的最优相关参数; 最后运用支持向量机的非线性运算能力完成了软件缺陷预测。仿真实验结果表明, 所提出的缺陷预测模型与传统的缺陷预测模型相比具有较高的预测准确率, 且预测速度更快。