Software Defect Prediction Method Research Articles

A software defect prediction method with metric compensation based on feature selection and transfer learning

A software defect prediction method with metric compensation based on feature selection and transfer learning

Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously

Software testing is the main method for finding software defects at present, and symmetric testing and other methods have been widely used, but these testing methods will cause a lot of waste of resources. Software defect prediction methods can reasonably allocate testing resources by predicting the defect tendency of software modules. Cross-project defect prediction methods have huge advantages when faced with missing datasets. However, most cross-project defect prediction methods are designed based on the settings of a single source project and a single target project. As the number of public datasets continues to grow, the number of source projects and defect information is increasing. Therefore, in the case of multi-source projects, this paper explores the problems existing when using multi-source projects for defect prediction. There are two problems. First, in practice, it is not possible to know in advance which source project is used to build the model to obtain the best prediction performance. Second, if an inappropriate source project is used in the experiment to build the model, it can lead to lower performance issues. According to the problems found in the experiment, the paper proposed a multi-source-based cross-project defect prediction method MSCPDP. Experimental results on the AEEEM dataset and PROMISE dataset show that the proposed MSCPDP method effectively solves the above two problems and outperforms most of the current state-of-art cross-project defect prediction methods on F1 and AUC. Compared with the six cross-project defect prediction methods, the F1 median is improved by 3.51%, 3.92%, 36.06%, 0.49%, 17.05%, and 9.49%, and the ACU median is improved by −3.42%, 8.78%, 0.96%, −2.21%, −7.94%, and 5.13%.

Defect Prediction Technology of Aerospace Software Based on Deep Neural Network and Process Measurement

In order to ensure high reliability, the efficiency of traditional aerospace software testing is often low. With the rapid development of machine learning, its powerful data feature extraction ability has great potential in improving the efficiency of aerospace software testing. Therefore, this paper proposed a software defect prediction method based on deep neural network and process measurement. Based on the NASA data set and combined with the software process data, the software defect measurement set is constructed. 35 measurement elements are used as the original input, and multiple single-layer automatic coding networks are superimposed to form the deep neural network model of software defect. The model is finally trained by the layer-by-layer greedy training method to realize software defect prediction. Experimental verification shows that the prediction method has a good prediction effect on aerospace software defects, and the accuracy rate reached 90%, which can greatly improve the efficiency and effect of aerospace software testing.

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.

Optimization of software defects prediction in imbalanced class using a combination of resampling methods with support vector machine and logistic regression

The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the < = 0.0344 < 0.05 and the > = 3.1524 > 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.

Deep Learning Software Defect Prediction Methods for Cloud Environments Research

This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.

Software defect prediction based on CS-BP neural network

Software defect prediction can detect whether there are defects in the program module so as to effectively reduce the unnecessary cost of software development and maintenance. In this paper, the limitation of the traditional BP neural network in the field of defect prediction leads to the inaccuracy of the prediction results. By using the global optimization ability of cuckoo search, the BP neural network is improved, the important initial parameters of the network are optimized, and the software defect prediction method of CS-BP is proposed. The experimental results show that compared with traditional machine learning algorithms such as BP neural network, J48 and SVM, CS-BP method has a better effect on the prediction of software defects.

A Novel Imbalanced Ensemble Learning in Software Defect Predication

With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the characteristics of imbalanced distribution of defect data sets have led to the deviation and loss of accuracy of most software defect prediction methods. This paper presents two novel approaches for learning from imbalanced data sets to produce a higher predictive accuracy over the minority class. These two methods differ in whether the oversampling and the misclassification cost information are utilized during training stage, and they are good at different aspects of imbalanced classification, one for dealing with highly imbalanced data sets and the other for moderately imbalanced data sets. Comparing with other state-of-the-art imbalance learning algorithms on imbalanced datasets, the experimental results show that these two methods have achieved excellent results in terms of G-mean and AUC measures, and more accurately identified the defective modules to reduce the cost of detection system.

Software Defect Prediction Based on Multivariate Statistical Method and Machine Learning Techniques

An essential objective of software system development is to find and fix defects before schedule delivery that would be expected below numerous circumstances. During software development, software engineers’ activities can result in software faults, causing disappointment in the future. Thus, the prediction of software defects within the initial stages has become an important interest in the field of software engineering. Many software defect prediction methods proposed in the last two decades. Mahalanobis-Taguchi System (MTS) is a multivariate statistical technique commonly used for feature selection and binary classification problems. In this article, the MTS is used for feature selection and defect prediction. This article compares several machine learning techniques on NASA data sets. The results show that MTS perform best with 100% accuracy, precision, recall, F-measure and 0% MAE, RMSE errors.

A software defect prediction method based on sampling and integration

This paper mainly analyzes the characteristics of software defect prediction from the perspective of machine learning, and proposes a semi-supervised software defect prediction method based on sampling and integration for the problem of class imbalance in software defect data and the incomplete classification of data sets. SISDP). SISDP firstly constructs a robust KNN marking model by taking a balanced sample of samples to mark a batch of unmarked data, and then iteratively adds the newly marked data to the original data set for the next marking model. , iterate until the data is marked. For the marked data set, the hybrid sampling algorithm is used to obtain the training set, and the integrated classification model composed of the multi-classification algorithm is classified and trained. SISDP not only reduces the interference of a few classes on the marking process, but also improves the generalization ability of the defect prediction model.

Semantic Feature Learning via Dual Sequences for Defect Prediction

Software defect prediction (SDP) can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. Existing methods often serialize an Abstract Syntax Tree (AST) obtained from the program source code into a token sequence, which is then inputted into the deep learning model to learn the semantic features. However, there are different ASTs with the same token sequence, and it is impossible to distinguish the tree structure of the ASTs only by a token sequence. To solve this problem, this paper proposes a framework called Semantic Feature Learning via Dual Sequences (SFLDS), which can capture the semantic and structural information in the AST for feature generation. Specifically, based on the AST, we select the representative nodes in the AST and convert the program source code into a simplified AST (S-AST). Our method introduces two sequences to represent the semantic and structural information of the S-AST, one is the result of traversing the S-AST node in pre-order, and another is composed of parent nodes. Then each token in the dual sequences is encoded as a numerical vector via mapping and word embedding. Finally, we use a bi-directional long short-term memory (BiLSTM) based neural network to automatically generate semantic features from the dual sequences for SDP. In addition, to leverage the statistical characteristics contained in the handcrafted metrics, we also propose a framework called Defect Prediction via SFLDS (DP-SFLDS) which combines the semantic features generated from SFLDS with handcrafted metrics to perform SDP. In our empirical studies, eight open-source Java projects from the PROMISE repository are chosen as our empirical subjects. Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.

Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information

To identify software modules that are more likely to be defective, machine learning has been used to construct software defect prediction (SDP) models. However, several previous works have found that the imbalanced nature of software defective data can decrease the model performance. In this paper, we discussed the issue of how to improve imbalanced data distribution in the context of SDP, which can benefit software defect prediction with the aim of finding better methods. Firstly, a relative density was introduced to reflect the significance of each instance within its class, which is irrelevant to the scale of data distribution in feature space; hence, it can be more robust than the absolute distance information. Secondly, a K-nearest-neighbors-based probability density estimation (KNN-PDE) alike strategy was utilised to calculate the relative density of each training instance. Furthermore, the fuzzy memberships of sample were designed based on relative density in order to eliminate classification error coming from noise and outlier samples. Finally, two algorithms were proposed to train software defect prediction models based on the weighted extreme learning machine. This paper compared the proposed algorithms with traditional SDP methods on the benchmark data sets. It was proved that the proposed methods have much better overall performance in terms of the measures including G-mean, AUC, and Balance. The proposed algorithms are more robust and adaptive for SDP data distribution types and can more accurately estimate the significance of each instance and assign the identical total fuzzy coefficients for two different classes without considering the impact of data scale.

Cost-sensitive Dictionary Learning for Software Defect Prediction

In recent years, software defect prediction has been recognized as a cost-sensitive learning problem. To deal with the unequal misclassification losses resulted by different classification errors, some cost-sensitive dictionary learning methods have been proposed recently. Generally speaking, these methods usually define the misclassification costs to measure the unequal losses and then propose to minimize the cost-sensitive reconstruction loss by embedding the cost information into the reconstruction function of dictionary learning. Although promising performance has been achieved, their cost-sensitive reconstruction functions are not well-designed. In addition, no sufficient attentions are paid to the coding coefficients which can also be helpful to reduce the reconstruction loss. To address these issues, this paper proposes a new cost-sensitive reconstruction loss function and introduces an additional cost-sensitive discrimination regularization for the coding coefficients. Both the two terms are jointly optimized in a unified cost-sensitive dictionary learning framework. By doing so, we can achieve the minimum reconstruction loss and thus obtain a more cost-sensitive dictionary for feature encoding of test data. In the experimental part, we have conducted extensive experiments on twenty-five software projects from four benchmark datasets of NASA, AEEEM, ReLink and Jureczko. The results, in comparison with ten state-of-the-art software defect prediction methods, demonstrate the effectiveness of learned cost-sensitive dictionary for software defect prediction.

Improved software defect prediction using Pruned Histogram-based isolation forest

Software defect prediction (SDP) is a hot topic in the modern software engineering research community. It has been used for evaluating software quality and reliability and allocating limited testing resources effectively. Based on analyzing the software source code and development process and extracting critical metrics, many data mining and machine learning methods have been used for SDP. However, these existing learning methods have difficulty with handling the imbalanced data distribution of accumulated training dataset. Isolation forest, an anomaly detection method based on the ensemble learning, has been studied to deal with the imbalanced data distribution issue for obtaining high prediction performance. However, the isolation forest method suffers from a main drawback of slow convergence, which is caused by selecting the feature value at random during the process of building isolation trees. To conquer this problem, in this paper histogram is constructed for the value set of selected isolation feature helping identify feature values preferable to build isolation trees. Motivated by the “many could be better than all” principle in the ensemble learning, the ensemble pruning strategy is further employed to optimize the obtained isolation forest, leading to a novel SDP method named PHIForest (Pruned Histogram-based Isolation Forest) in this work. The proposed method can provide fast convergence through the histogram-based splitting feature value selection, and decrease the ensemble scale and improve prediction performance through the ensemble pruning. Comprehensive experiments conducted on ten real datasets are performed to demonstrate effectiveness of the proposed SDP method.

Process metrics for software defect prediction in object‐oriented programs

Software evolution is an important activity in the life cycle of a modern software system. In the process of software evolution, the repair of historical defects and the increasing demands may introduce new defects. Therefore, evolution-oriented defect prediction has attracted much attention of researchers in recent years. At present, some researchers have proposed the process metrics to describe the characteristics of software evolution. However, compared with the traditional software defect prediction methods, the research on evolution-oriented defect prediction is still inadequate. Based on the evolution data of object-oriented programs, this study presented two new process metrics from the defect rates of historical packages and the change degree of classes. To show the effectiveness of the proposed process metrics, the authors made comparisons with the code metrics and other process metrics. An empirical study was conducted on 33 versions of nine open-source projects. The results showed that adding the proposed process metrics could improve the performance of evolution-oriented defect prediction effectively.

PathPair2Vec: An AST path pair-based code representation method for defect prediction

Abstract Software project defect prediction (SDP) can predict the bug probability of software by their features and allocate their testing efforts. The existing software defect prediction methods can be divided into two categories: methods based on traditional handcrafted features and methods based on automatically made abstract features, especially those made by deep learning. The current research indicates that deep learning-based automatic features can achieve better performance than handcrafted features. Code2vec (Alon et al. 2019) is one of the best source code representation models, which leverages deep learning to learn automatic representations from code. In this paper, inspired by code2vec, we propose a new AST path pair-based source code representation method (PathPair2Vec) and apply it to software project defect prediction. We first propose the concept of the short path to describe each terminal node and its control logic. Then, we design a new sequence encoding method to code the different parts of the terminal node and its control logic. Finally, by pairs of short paths, we describe the semantic information of code and fuse them by an attention mechanism. Experiments on the PROMISE dataset show that our method improves the F1 score by 17.88% over the state-of-the-art SDP method, and the AST path pair-based source code representation can better identify the defect features of the source code.

A Feature Selection Framework for Software Defect Prediction Using ISFLA

Aiming at the problem of feature space dimension reduction and large search space in feature selection of software defect, a defect prediction feature selection framework based on meta-heuristic search algorithm (ISFLA) is proposed. The framework improves generalization of predictions of unknown data samples, enhances the ability to search for features related to learning tasks, and completes further reductions in feature space dimensions. Using some NASA data sets, several common software defect prediction methods and ISFLA simulation experiments were carried out. The experimental results show that the software feature selection framework based on the improved shuffled frog leaping algorithm effectively improves the performance of software defect prediction.

An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction

Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.

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.