Software Defect Prediction Method Research Articles

Software defect prediction using over-sampling and feature extraction based on Mahalanobis distance

As the size of software projects becomes larger, software defect prediction (SDP) will play a key role in allocating testing resources reasonably, reducing testing costs, and speeding up the development process. Most SDP methods have used machine learning techniques based on common software metrics such as Halstead and McCabe’s cyclomatic. Datasets produced by these metrics usually do not follow Gaussian distribution, and also, they have overlaps in defect and non-defect classes. In addition, in many of software defect datasets, the number of defective modules (minority class) is considerably less than non-defective modules (majority class). In this situation, the performance of machine learning methods is reduced dramatically. Therefore, we first need to create a balance between minority and majority classes and then transfer the samples into a new space in which pair samples with same class (must-link set) are near to each other as close as possible and pair samples with different classes (cannot-link) stay as far as possible. To achieve the mentioned objectives, in this paper, Mahalanobis distance in two manners will be used. First, the minority class is oversampled based on the Mahalanobis distance such that generated synthetic data are more diverse from other minority data, and minority class distribution is not changed significantly. Second, a feature extraction method based on Mahalanobis distance metric learning is used which try to minimize distances of sample pairs in must-links and maximize the distance of sample pairs in cannot-links. To demonstrate the effectiveness of the proposed method, we performed some experiments on 12 publicly available datasets which are collected NASA repositories and compared its result by some powerful previous methods. The performance is evaluated in F-measure, G-Mean, and Matthews correlation coefficient. Generally, the proposed method has better performance as compared to the mentioned methods.

An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search

SummaryBoth the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi‐objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non‐defective sampling and optimize the parameters of SVM. Then, three under‐sampled methods for decision region range are proposed to select the non‐defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G‐mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.

Unsupervised software defect prediction using median absolute deviation threshold based spectral classifier on signed Laplacian matrix

Area of interestThe trend of current software inevitably leads to the big data era. There are much of large software developed from hundreds to thousands of modules. In software development projects, finding the defect proneness manually on each module in large software dataset is probably inefficient in resources. In this task, the use of a software defect prediction model becomes a popular solution with much more cost-effective rather than manual reviews. This study presents a specific machine learning algorithm, which is the spectral classifier, to develop a software defect prediction model using unsupervised learning approach.Background and objectiveThe spectral classifier has been successfully used in software defect prediction because of its reliability to consider the similarities between software entities. However, there are conditional issues when it uses the zero value as partitioning threshold. The classifier will produce the predominantly cluster when the eigenvector values are mostly positives. Besides, it will also generate low clusters compactness when the eigenvector contains outliers. The objective of this study is mainly to propose an alternative partitioning threshold in dealing with the zero threshold issues. Generally, the proposed method is expected to improve the spectral classifier based software defect prediction performances.MethodsThis study proposes the median absolute deviation threshold based spectral classifier to carry out the zero value threshold issues. The proposed method considers the eigenvector values dispersion measure as the new partitioning threshold, rather than using a central tendency measure (e.g., zero, mean, median). The baseline method of this study is the zero value threshold based spectral classifier. Both methods are performed on the signed Laplacian matrix to meet the non-negative Laplacian graph assumption. For classification, the heuristic row sum method is used to assign the entity class as the prediction label.Results and conclusionIn terms of clustering, the proposed method can produce better cluster memberships that affect the cluster compactness and the classifier performances improvement. The cluster compactness average of both the proposed and baseline methods are 1.4 DBI and 1.8 DBI, respectively. In classification performance, the proposed method performs better accuracy with lower error rates than the baseline method. The proposed method also has high precision but low in the recall, which means that the proposed method can detect the software defect more precisely, although in the small number in detection. The proposed method has the accuracy, precision, recall, and error rates with average values of 0.79, 0.84, 0.72, and 0.21, respectively. While the baseline method has the accuracy, precision, recall, and error rates with average values of 0.74, 0.74, 0.89, and 0.26, respectively. Based on those results, the proposed method able to provide a viable solution to address the zero threshold issues in the spectral classifier. Hence, this study concludes that the use of the median absolute deviation threshold can improve the spectral based unsupervised software defect prediction method.

Software Defect Prediction Based on Cost-Sensitive Dictionary Learning

Software defect prediction technology has been widely used in improving the quality of software system. Most real software defect datasets tend to have fewer defective modules than defective-free modules. Highly class-imbalanced data typically make accurate predictions difficult. The imbalanced nature of software defect datasets makes the prediction model classifying a defective module as a defective-free one easily. As there exists the similarity during the different software modules, one module can be represented by the sparse representation coefficients over the pre-defined dictionary which consists of historical software defect datasets. In this study, we make use of dictionary learning method to predict software defect. We optimize the classifier parameters and the dictionary atoms iteratively, to ensure that the extracted features (sparse representation) are optimal for the trained classifier. We prove the optimal condition of the elastic net which is used to solve the sparse coding coefficients and the regularity of the elastic net solution. Due to the reason that the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making the classification inclining to classify a module as a defective one. Thus, we propose a cost-sensitive software defect prediction method using dictionary learning (CSDL). Experimental results on the 10 class-imbalance datasets of NASA show that our method is more effective than several typical state-of-the-art defect prediction methods.

Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a practical solution that allows software defect prediction (SDP) to be used earlier in the software lifecycle. With the CPDP technique, the software defect predictor trained by labeled data of mature projects can be applied for the prediction task of a new project. Most previous CPDP approaches ignored the semantic information in the source code, and existing semantic-feature-based SDP methods do not take into account the data distribution divergence between projects. These limitations may weaken defect prediction performance. To solve these problems, we propose a novel approach, the transfer convolutional neural network (TCNN), to mine the transferable semantic (deep-learning (DL)-generated) features for CPDP tasks. Specifically, our approach first parses the source file into integer vectors as the network inputs. Next, to obtain the TCNN model, a matching layer is added into convolutional neural network where the hidden representations of the source and target project-specific data are embedded into a reproducing kernel Hilbert space for distribution matching. By simultaneously minimizing classification error and distribution divergence between projects, the constructed TCNN could extract the transferable DL-generated features. Finally, without losing the information contained in handcrafted features, we combine them with transferable DL-generated features to form the joint features for CPDP performing. Experiments based on 10 benchmark projects (with 90 pairs of CPDP tasks) showed that the proposed TCNN method is superior to the reference methods.

Defect Prediction Model for Object Oriented Software Based on Particle Swarm Optimized SVM

In terms of the security problem of power information system, this paper analysed the importance of the software defect prediction method in object-oriented software development, and proposed a software prediction model based on particle swarm optimized Support Vector Machine (SVM) corresponding to the features of object-oriented software. The model mainly consists of three parts: the first is the pre-processing module which normalizes the original data and selects feature, then the second is adaptive inertia weight particle swarm module which optimizes the parameters of SVM with the prediction accuracy as the fitness. Finally, the last SVM classification module predicts categories of reduced-dimension data using the optimal parameters from the second module. Experimental results show that the accuracy of the proposed model is 8.2%—12.2% higher than the comparative model, and 9.9%, 5.6% and 7.7% higher on the precision, recall and F value, which proves the validity of the proposed model.

Efficient Prediction Method of Defect of Monitor Configuration Software

In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.

A Framework for Software Defect Prediction and Metric Selection

Automated software defect prediction is an important and fundamental activity in the domain of software development. However, modern software systems are inherently large and complex with numerous correlated metrics that capture different aspects of the software components. This large number of correlated metrics makes building a software defect prediction model very complex. Thus, identifying and selecting a subset of metrics that enhance the software defect prediction method’s performance are an important but challenging problem that has received little attention in the literature. The main objective of this paper is to identify significant software metrics, to build and evaluate an automated software defect prediction model. We propose two novel hybrid software defect prediction models to identify the significant attributes (metrics) using a combination of wrapper and filter techniques. The novelty of our approach is that it embeds the metric selection and training processes of software defect prediction as a single process while reducing the measurement overhead significantly. Different wrapper approaches were combined, including SVM and ANN, with a maximum relevance filter approach to find the significant metrics. A filter score was injected into the wrapper selection process in the proposed approaches to direct the search process efficiently to identify significant metrics. Experimental results with real defect-prone software data sets show that the proposed hybrid approaches achieve significantly compact metrics (i.e., selecting the most significant metrics) with high prediction accuracy compared with conventional wrapper or filter approaches. The performance of the proposed framework has also been verified using a statistical multivariate quality control process using multivariate exponentially weighted moving average. The proposed framework demonstrates that the hybrid heuristic can guide the metric selection process in a computationally efficient way by integrating the intrinsic characteristics from the filters into the wrapper and using the advantages of both the filter and wrapper approaches.

Non‐negative sparse‐based SemiBoost for software defect prediction

SummarySoftware defect prediction is an important decision support activity in software quality assurance. The limitation of the labelled modules usually makes the prediction difficult, and the class‐imbalance characteristic of software defect data leads to negative influence on decision of classifiers. Semi‐supervised learning can build high‐performance classifiers by using large amount of unlabelled modules together with the labelled modules. Ensemble learning achieves a better prediction capability for class‐imbalance data by using a series of weak classifiers to reduce the bias generated by the majority class. In this paper, we propose a new semi‐supervised software defect prediction approach, non‐negative sparse‐based SemiBoost learning. The approach is capable of exploiting both labelled and unlabelled data and is formulated in a boosting framework. In order to enhance the prediction ability, we design a flexible non‐negative sparse similarity matrix, which can fully exploit the similarity of historical data by incorporating the non‐negativity constraint into sparse learning for better learning the latent clustering relationship among software modules. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that non‐negative sparse‐based SemiBoost learning outperforms several representative state‐of‐the‐art semi‐supervised software defect prediction methods. Copyright © 2016 John Wiley & Sons, Ltd.

Label propagation based semi-supervised learning for software defect prediction

Software defect prediction can automatically predict defect-prone software modules for efficient software test in software engineering. When the previous defect labels of modules are limited, predicting the defect-prone modules becomes a challenging problem. In static software defect prediction, there exist the similarity among software modules, a software module can be approximated by a sparse representation of the other part of the software modules, and class-imbalance problem, the number of defect-free modules is much larger than that of defective ones. In this paper, we propose to use graph based semi-supervised learning technique to predict software defect. By using Laplacian score sampling strategy for the labeled defect-free modules, we construct a class-balance labeled training dataset firstly. And then, we use a nonnegative sparse algorithm to compute the nonnegative sparse weights of a relationship graph which serve as clustering indicators. Lastly, on the nonnegative sparse graph, we use a label propagation algorithm to iteratively predict the labels of unlabeled software modules. We thus propose a nonnegative sparse graph based label propagation approach for software defect classification and prediction, which uses not only few labeled data but also abundant unlabeled ones to improve the generalization capability. We vary the size of labeled software modules from 10 to 30 % of all the datasets in the widely used NASA projects. Experimental results show that the NSGLP outperforms several representative state-of-the-art semi-supervised software defect prediction methods, and it can fully exploit the characteristics of static code metrics and improve the generalization capability of the software defect prediction model.

Research on Software Defect Prediction Method Based on Machine Learning

This paper analyzed 44 metrics of application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correlation coefficient (SRCC) between the predicting defect density and the actual defect density based on SVR model is 0.6727, higher than other 15 machine learning models, the model that has the second absolute value of SRCC is IBk model, the SRCC only is-0.3557, the results show that the method based on SVR has the highest prediction accuracy.

Implementation of Elliptic Curve Digital Signature Algorithms

Recent studies of software defect prediction typically produce datasets, methods and frameworks which allow software engineers to focus on development activities in terms of defect-prone code, thereby improving software quality and making better use of resources. Many software defect prediction datasets, methods and frameworks are published disparate and complex, thus a comprehensive picture of the current state of defect prediction research that exists is missing. This literature review aims to identify and analyze the research trends, datasets, methods and frameworks used in software defect prediction research betweeen 2000 and 2013. Based on the defined inclusion and exclusion criteria, 71 software defect prediction studies published between January 2000 and December 2013 were remained and selected to be investigated further. This literature review has been undertaken as a systematic literature review. Systematic literature review is defined as a process of identifying, assessing, and interpreting all available research evidence with the purpose to provide answers for specific research questions. Analysis of the selected primary studies revealed that current software defect prediction research focuses on five topics and trends: estimation, association, classification, clustering and dataset analysis. The total distribution of defect prediction methods is as follows. 77.46% of the research studies are related to classification methods, 14.08% of the studies focused on estimation methods, and 1.41% of the studies concerned on clustering and association methods. In addition, 64.79% of the research studies used public datasets and 35.21% of the research studies used private datasets. Nineteen different methods have been applied to predict software defects. From the nineteen methods, seven most applied methods in software defect prediction are identified. Researchers proposed some techniques for improving the accuracy of machine learning classifier for software defect prediction by ensembling some machine learning methods, by using boosting algorithm, by adding feature selection and by using parameter optimization for some classifiers. The results of this research also identified three frameworks that are highly cited and therefore influential in the software defect prediction field. They are Menzies et al. Framework, Lessmann et al. Framework, and Song et al. Framework.